Downloads 2026
Number of events: 6617
- $A_2$DEPT: Large Language Model–Driven Automated Algorithm Design via Evolutionary Program Trees
- $\alpha$-PFN: Fast Entropy Search via In-Context Learning
- $E^2$PO: Embedding-perturbed Exploration Preference Optimization for Flow Models
- $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses
- $f$-Divergence Self-Play for Tabular Anomaly Detection via Large Language Models
- $f$-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
- $G^2$-Reader: Dual Evolving Graphs for Multimodal Document QA
- $L^3$: Large Lookup Layers
- $\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval
- $\mathcal{O}(\log N)$ Latent Dimension Suffices for Universal Approximation of Permutation-invariant Function
- $\mu$pscaling small models: Principled warm starts and hyperparameter transfer
- $\phi$-Balancing for Mixture-of-Experts Training
- $R^3$DAO: Reactive Recovery and Reconstruction for Long-horizon Data Agent Orchestration
- $\sigma$: Sigmoid Modulation for Ultra High Resolution Diffusion
- $\tau$-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
- $\tau$-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- $\text{DT}^\text{2}$: Decision-Targeted Digital Twins
- $\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
- $\texttt{FlashSchNet}$: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics
- $\texttt{IDEAS}$: Interpretability Driven Evolutionary Approach for the Design of Biological Sequences
- $\texttt{MetaDistill}$: Unlocking the Performance Ceiling for Pretrained Optimizers
- $\texttt{Multi}^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments
- $\texttt{PRISM}$:A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
- $\texttt{ShaplEIG}$: Bayesian Experimental Design for Shapley Value Estimation
- $V_0$: A Generalist Value Model for Any Policy at State Zero
- 1-Bit Wonder: Improving QAT Performance in the Low-Bit Regime through K-Means Quantization
- (1D) Ordered Tokens Enable Efficient Test-Time Search
- 2nd Workshop on Compositional Learning: Safety, Interpretability, and Agents
- 3D-DLP: Self-supervised 3D Object-centric Scene Representation Learning
- 3DGS$^2$-TR: A Scalable Second-Order Trust-Region Method for 3D Gaussian Splatting
- 3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification
- 3D MeanFlow: One-Step Point Cloud Completion and Generation via Average-Velocity Transport
- 3DMedAgent: Unified Perception-to-Understanding for 3D Medical Analysis
- 3DPoV: Improving 3D understanding via Patch Ordering on Videos
- 3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding
- 3D Scene Assertion Verification
- 3rd AI for Math Workshop: Toward Self-Evolving Scientific Agents
- 3rd Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences
- 3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models
- 4DPC$^2$hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping
- 4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere
- 4th Structured Probabilistic Inference & Generative Modeling
- A$^2$SG: Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Network
- A²RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation
- A3: an Analytical Low-Rank Approximation Framework for Attention
- AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
- A Bayesian Approach to Quantify the Uncertainty of Human Ratings in a Single-Instance Multimodal Framework
- ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity
- ABCD: All Biases Come Disguised
- Abductive Reasoning with Probabilistic Commonsense
- A Behavioural and Representational Evaluation of Goal-Directedness in Language Model Agents
- A Benchmark and Framework for Evaluating Next Action Predictions in Spreadsheets
- A Bi-metric Framework for Efficient Nearest Neighbor Search
- ABSINT-AI: Agentic Heap Abstractions for Abstract Interpretation
- Absorbing Quantization Error by Deformable Noise Scheduler for Diffusion Models
- Abstraction Induces the Brain Alignment of Language and Speech Models
- A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
- A Capacity-Based Rationale for Multi-Head Attention
- A Cartesian-3j and nj Framework for Machine Learning Interatomic Potentials
- Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
- Accelerated Dual Method for Distributed Optimization: An Inexact-Gradient View of Local Updates
- Accelerated Multiple Wasserstein Gradient Flows for Multi-objective Distributional Optimization
- Accelerating Langevin Monte Carlo via Efficient Stochastic Runge-Kutta Methods beyond Log-Concavity
- Accelerating Q-learning through Efficient Value-sharing across Actions
- Accelerating Regression Tasks with Quantum Algorithms
- Accordion-Thinking: Self-Regulated Step Summaries for Efficient and Readable LLM Reasoning
- Accuracy and Normalized Accuracy under Length Bias: Analysis, Guidelines, and a Bayesian Alternative
- Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
- Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
- Accurate Large-scale Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
- Accurate, private, secure, federated U-statistics with higher degree
- Achieving Logarithmic Regret in KL-Regularized Zero-Sum Markov Games
- Achieving Structurally Robust Gromov Wasserstein Distance via Adaptive Dual-Mask
- A Close Look at Negative Label Guided Out-of-distribution Detection in Pre-trained Vision-Language Models
- AC-ODM: Actor–Critic Online Data Mixing for Sample-Efficient LLM Pretraining
- A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
- ACO-MoE-LoRA: Evolving-while-Training for Adapting Segment Anything Model 2 to Specialized Domains
- A Computational Framework for Evaluating Human-likeness in LLMs' Open-ended Human Behaviors
- A Conflict-aware Evidential Framework for Reliable Sleep Stage Classification
- ACON: Optimizing Context Compression for Long-horizon LLM Agents
- A Consensus Anchor-guided Hypergraph Framework For Incomplete Multi-view Clustering
- A Constrained Optimization Perspective of Unrolled Transformers
- A Control-Theoretic View of Mamba on Stability and Robustness
- Acoustic Interference: A New Paradigm Weaponizing Acoustic Latent Semantic for Universal Jailbreak against Large Audio Language Models
- A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn’t)
- ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
- Action Manifold Smoothing: A Lipschitz Pathway Perspective on High-Dimensional Reinforcement Learning
- Action-Sufficient Goal Representations
- Activation-Free Backbones for Image Recognition: Polynomial Alternatives for Spatial and Channel Mixing
- Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers
- Activation with Intrinsic-Extrinsic Consensus
- Active Attacks: Red-teaming LLMs via Adaptive Environments
- Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
- Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
- Active Curriculum Refinement for Reinforcement Learning
- Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models
- Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
- Active Learning with Low-Rank Structure for Data Selection
- ACTIVE-o3 : Empowering MLLMs with Active Perception via Pure Reinforcement Learning
- Active Policy Optimization for Individualized Dosing via Gradient Variance Minimization
- Active Reasoning Vision-Language Model via Sequential Experimental Design
- Active Regression for Single-Index Models with Unknown Link Functions
- ActiveScope: Actively Seeking and Correcting Perception for MLLMs
- Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
- Active Timepoint Selection for Learning Measure-Valued Trajectories
- ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
- AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
- AdaGC: Enhancing LLM Pretraining Stability via Adaptive Gradient Clipping
- AdaHC: Accelerating Multi-Token Prediction with Adaptive Head Chunking with Pipeline Parallelism
- AdaMEM: Test-Time Adaptive Memory for Language Agents
- AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
- AdamO: A Collapse-Suppressed Optimizer for Offline RL
- AdaNav: Adaptive Reasoning with Uncertainty for Vision-Language Navigation
- AdaptFM: Resource-Adaptive Foundation Model Inference
- Adapting Noise to Data: Generative Flows from Learned 1D Processes
- Adapting to Evolving Graphs: A Scalable Framework for Dynamic Coarsening
- Adaptive Bandit Algorithms for Contextual Matching Markets
- Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent
- Adaptive Code Watermarking Through Reinforcement Learning
- Adaptive Contracts for Cost-Effective AI Delegation
- Adaptive DNA Sequence Modeling via Synergistic Plasticity Units
- Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
- Adaptive Generation of Bias-Eliciting Questions for LLMs
- Adaptive Group Elicitation via Multi-Turn LLM Interactions
- Adaptively Grouped Contextual Bandits for Heterogeneous Human-AI Decision Making with Conformal Prediction Sets
- Adaptively Robust Resettable Streaming
- Adaptive Memory Retention in Dynamic Graphs
- Adaptive Momentum and Nonlinear Damping for Neural Network Training
- Adaptive Multi-Round Allocation with Stochastic Arrivals
- Adaptive Multiscale Binary Expansion Tests for Independence
- Adaptive Node Feature Selection for Graph Neural Networks
- Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
- Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems
- Adaptive Policy Backbone via Shared Network
- Adaptive Preconditioners Trigger Loss Spikes in Adam
- Adaptive Probe-based Steering for Robust LLM Jailbreaking
- Adaptive Protein Tokenization
- Adaptive Quasimetric Mapping : Principled Topological Abstraction for Robust Offline Goal-Conditioned Navigation
- Adaptive Querying with AI Persona Priors
- Adaptive Recurrent Message Passing for Test Time Computing on Graphs
- Adaptive Reinforcement Learning for Unobservable Random Delays
- Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision-Language Models
- Adaptive Sharpness-Aware Minimization with a Polyak-type Step size: A Theory-Grounded Scheduler
- Adaptive Symmetry Discovery for Dynamical System Identification
- Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks
- Adaptive Time Series Reasoning via Segment Selection
- Adaptive Token Refinement in Long-Tailed Large Vision-Language Models Fine-Tuning
- Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
- Adaptive Visual Autoregressive Acceleration via Dual-Linkage Entropy Analysis
- Adaptive Volumetric Mechanical Property Fields Invariant to Resolution
- AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally
- AdaS: Adaptive Gradient Descent for Spiking Transformers
- AdaSCALE: Adaptive Scaling for OOD Detection
- AdaSplash-2: Faster Differentiable Sparse Attention
- AD-BTS: Adaptive Dual-Branch Token Sparsification via Spatial Information Density
- Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
- Addressing Semantic Blind Spots in Text-to-SQL via Component Pre-generation and AST Matching Rewards
- A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt
- A Deep Learning Model of Mental Rotation Informed by Interactive VR Experiments
- ADEPT: RL-Aligned Agentic Decoding of Emotion via Evidence Probing Tools — From Consensus Learning to Ambiguity-Driven Emotion Reasoning
- ADHD Disease Detection Based on Short- and Long-Term Brain Function Encoding and Memory Graph Network
- A Diagnostic Study of Multi-Agent LLMs for Real-World Debates
- A Diffusive Classification Loss for Learning Energy-based Generative Models
- A Dirac-Frenkel-Onsager principle: Instantaneous residual minimization with gauge momentum for nonlinear parametrizations of PDE solutions
- A Direct Approach for Handling Contextual Bandits with Latent State Dynamics
- A Direct Second-Order Method for Solving Two-Player Zero-Sum Games
- A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle
- AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing
- AD-MIR: Bridging the Gap from Perception to Persuasion in Advertising Video Understanding via Structured Reasoning
- Advancing Analytic Class-Incremental Learning through Vision-Language Calibration
- Advancing LLM Reasoning with Natural Language and Numerical Feedback
- Advancing SVD-based LLM Compression via Layer-Wise Error Model Search
- Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation
- Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models
- AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search
- Adversarial Attack and Defense for Denoising Diffusion Sampling
- Adversarial Attacks and Robust Training for Hypergraph Neural Networks
- Adversarial Flow Models
- Adversarial Latent Embedding Repair for LLM Continual Learning
- Adversarially Robust Approximate Furthest Neighbor
- Adversarially Robust Control of Conditional Value-at-Risk via Kelly Conformal Inference
- Adversarial Reinforcement Learning for Robust Diffusion Large Language Model Unlearning
- Adversarial Robustness of Implicit Neural Representation-Based Classifiers
- Adversarial Training for Process Reward Models
- Adversarial Vulnerability from Interference Between Features in Superposition
- AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
- AES: Curing Optimizer Blindness in Long-Tailed Recognition via State-Aware Correction
- AesFormer: Transform Everyday Photos into Beautiful Memories
- A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity
- A Fast and Soft Pattern Matcher for Trillion-Scale Corpus
- Affine-Equivariant Kernel Space Encoding for NeRF Editing
- Affine-Scaled Attention: Towards Flexible and Stable Transformer Attention
- AffIn-Space: Learning Affine-Invariant Representations for 3D Spatial Understanding with MLLMs
- A Fine-Grained Understanding of Uniform Convergence for Halfspaces
- A Flat Vocabulary or a Rich Hierarchy? Re-introducing Intrinsic Structure Transforms the Autoregressive Image Generation
- A Formal Comparison Between Chain of Thought and Latent Thought
- A Foundation-style Model for Zero-Shot Statistical Dependency Measurement
- A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
- A Framework for Understanding Learnability in Transformers
- A Fully First-Order Layer for Differentiable Optimization
- A Game-Theoretic Framework for Measuring and Explaining Metric Compatibility in Fair Machine Learning
- A General Framework for Dynamic Consistent Submodular Maximization
- A General Framework for Fair and Robust Regression
- A Generalist Pair-wise Progress Critic Model for Vision-Language-Action Robots
- A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention
- Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
- AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation
- AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent
- AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
- Agentic Confidence Calibration
- Agentic Framework for Epidemiological Modeling
- Agentic Model Predictive Questioning Control in Visual Design
- Agentic Monte Carlo: Reinforcement Learning for Black-Box LLM Agents
- Agentic Proposing: Enhancing Large language Model Reasoning via Compositional Skill Synthesis
- Agent JIT Compilation for Latency-Optimizing Computer-Use Agent Planning and Scheduling
- AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks
- Agent Learning via Early Experience
- AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition
- Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning
- Agent Primitives: Reuseable Latent Building Blocks for Multi-Agent Systems
- AgentScore: Autoformulation of Deployable Clinical Scoring Systems
- AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
- AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech
- AgentTailor: A Semantic-Aware LLM-Based Multi-Agent System with Actor-Critic Structure
- AgentVocab: Structure-Aware Vocabulary Adaptation for Efficient LLM Agents
- AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web
- Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning
- AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
- A Geometric Analysis of Small-sized Language Model Hallucinations
- A Geometric Lens on Physics-Aligned Data Compression
- A geometric relation of the error introduced by sampling a language model's output distribution to its internal state
- A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization
- A Geometry-Based View of Mahalanobis OOD Detection
- Aggregate Models, Not Explanations: Improving Feature Importance Estimation
- AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
- Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents
- A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
- A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
- AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching
- AGZO: Activation-Guided Zeroth-Order Optimization for LLM Fine-Tuning
- A Hitchhiker's Guide to Poisson Gradient Estimation
- A Hypertoroidal Covering for Perfect Color Equivariance
- AI4Physics: An ICML 2026 Workshop on AI for Physics
- AI as a Tool for Mathematics, Computer Science, and Machine Learning
- AICrypto: Evaluating Cryptography Capabilities of Large Language Models
- AI Engram: In Search of Memory Traces in Artificial Intelligence
- AI for Law Workshop
- AI for Science: AI Scientists -- Tools, Co-authors, or Founders?
- AIR: Improving Agent Safety through Incident Response
- AIR: Post-training Data Selection for Reasoning via Attention Head Influence
- AIR-VLA: Vision-Language-Action Systems for Aerial Manipulation
- Aitchison Embeddings for Learning Compositional Graph Representations
- A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth
- A Kinetic-Energy Perspective of Flow Matching
- A KL-regularization framework for learning to plan with adaptive priors
- A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search
- ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
- Alethia: a Foundational Encoder for Voice Deepfakes
- Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models
- Algorithmic Recourse of In-Context Learning for Tabular Data
- AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms
- AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs
- AlignedNorm: Prompting Vision–Language Models via Coupled Prompt Field
- Align Forward, Adapt Backward: Closing the Discretization Gap in Logic Gate Networks
- Aligning Datasets and Models for Weight Space Learning
- Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs
- Alignment-Aware Decoding
- Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories
- Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models
- Alignment Pretraining: AI Discourse Causes Self-Fulfilling (Mis)alignment
- Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels
- Alignment Tampering: How Reinforcement Learning from Human Feedback Is Exploited to Optimize Misaligned Biases
- AlignVid: Taming Visual Dominance via Training-Free Attention Modulation in Text-guided Image-to-Video Generation
- Align Your Trajectory Tangent: Training Better Consistency Models via Manifold-Aligned Tangents
- AliMark: Enhancing Robustness of Sentence-Level Watermarks Against Text Paraphrasing
- A Linear Expectation Constraint for Selective Prediction and Routing with False-Discovery Control
- A Linearly Convergent Proximal Subgradient Algorithm for Sparse Portfolio Optimization with Transaction Cost
- All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs
- All ERMs Can Fail in Stochastic Convex Optimization Lower Bounds in Linear Dimension
- Alleviating Observation Bias via Causal-Invariant Meta-Learning for Unbalanced Incomplete Multi-view Clustering
- Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO
- Allocating Variance to Maximize Expectation
- AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in Unified Multimodal Models via Decompositional Verifiable Reward
- AlphaRouter: Token-level Routing Between SLM and LLM with Reinforcement Learning and Tree Search
- ALSO: Adversarial Online Strategy Optimization for Social Agents
- Alterbute: Editing Intrinsic Attributes of Objects in Images
- Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training
- AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
- A Machine-Learned Comorbidity Index
- Ambient Dataloops: Generative Models for Dataset Refinement
- Ambiguous Strategic Classification
- AmbiRefer3D: 3D Visual Grounding with Referential Ambiguity
- AMDP: Asynchronous Multi-Directional Pipeline Parallelism for Large-Scale Models Training
- A Mechanistic Understanding of Sim-and-Real Co-Training in Generative Policies
- A Minimal Agent for Automated Theorem Proving
- A Minimax Approach for Optimal Intervention Policy Learning with Two-Stage Outcomes
- Amodal Instance Segmentation with IRAIS Dataset for Sim-to-Real Transfer
- A model of errors in transformers
- Amortized Maximum Inner Product Search with Learned Support Functions
- Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
- Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation
- An Algebraic View of the Expressivity of Recurrent Language Models
- AnalogVerifier: A Neuro-Symbolic Framework for Analog Circuit Verification
- Analytic Bijections for Smooth and Interpretable Normalizing Flows
- An analytic theory of convolutional neural network inverse problems solvers
- An Approximation Algorithm for Graph Label Selection
- A Narrowing Geometry in Contaminated Reasoning
- An Asymmetric Latent Factorization-of-Tensors Model for Relation Extraction
- Anatomy of Massive Activations and Attention Sinks
- ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models
- ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm
- Anchored Policy Optimization: Mitigating Exploration Collapse via Support-Constrained Rectification
- Anchor-Final Self-Supervision Drives Hallucination-Aware Optimization in Large Vision-Language Models
- Anchor-guided Hypergraph Condensation with Dual-level Discrimination
- Anchoring Self-Play for Code Repair
- An Efficient Joint Learning Approach for Item Response Theory
- An Embarrasingly Simple Way to Optimize Orthogonal Matrices at Scale
- An Empirical Study of Memory Poisoning Defenses for LLM Agents
- An Empirical Study on the Resilience of Partial Merging to Model Clone Attacks
- An Evidential Route to Asymptotic Bayes Optimality under Sparsity
- A New Framework for Cybersecurity Refusals in AI Agents
- An Exploration of Non-Euclidean Gradient Descent: Muon and its Many Variants
- An Exponential Separation Between Quantum and Quantum-Inspired Classical Algorithms for Linear Systems
- An Exterior Method for Nonnegative Matrix Factorization
- Angel or Demon: Investigating the Plasticity Interventions' Impact on Backdoor Threats in Deep Reinforcement Learning
- An In-Depth Study on Deep Learning Model Cloning
- An Information-Theoretic Criterion for Efficient Data Synthesis
- An Interactive Paradigm for Deep Research
- Annotations Mitigate Post-Training Mode Collapse
- An Odd Estimator for Shapley Values
- A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models
- Anomaly-Preference Image Generation
- AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
- Anti-Aliasing Matters: A Dynamic Network for Time Series Forecasting
- Anti-Backdoor Coreset Selection via Cumulative Entropy
- ANTiC: Adaptive Neural Temporal In Situ Compressor
- Anti-causal domain generalization: Leveraging unlabeled data
- Antidistillation Fingerprinting
- Any2Any: Unified Arbitrary Modality Translation for Remote Sensing
- Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds
- AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors
- AnyCanvas: Potential Field Guidance for Training-Free Spatial Control in Text-to-Image Diffusion
- Any-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion
- Any-dimensional invariant universality
- AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise
- AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference
- Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
- Anytime Safe PAC Efficient Reasoning
- Anytime-Valid Inference for Online Ranking of Large Language Models
- Anytime-Valid Inference Under Outcome Delay: A Design-Based Approach
- AOEB: Benchmarking Agent-Oriented Multimodal Embeddings
- AOEPT: Breaking the Implicit Modality-Reduction Bottleneck in Modality Missing Prompt Tuning
- AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration
- APE-Bench: Evaluating Automated Proof Engineering for Formal Math Libraries
- A Penalty Approach For Differentiation Through Black-box Quadratic Programming Solvers
- A Perturbation Approach to Unconstrained Linear Bandits
- APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries
- API: Adaptive Prototype Imputation for Incomplete Multimodal Sentiment Analysis
- APIC: Orthogonalized Neuro-Symbolic Modeling for Nonlinear Dissipative Dynamics
- A Positive Case for Faithfulness: Explanations Help Predict Model Behavior
- Approximate Equivariance via Projection-Based Regularisation
- Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach
- Approximate Proportionality in Online Fair Division
- Approximating Drift-Diffusion Models for User Decisions under Nudging and External Information
- Approximating f -Divergences with Rank Statistics
- Approximation Bounds for Transformer Networks with Application to Regression
- Approximation Error Upper and Lower Bounds for Hölder Class with Transformers
- Approximation of Log-Partition Function in Policy Mirror Descent Induces Implicit Regularization for LLM Post-Training
- Approximation Preserving Coresets
- Approximation Theory for Lipschitz Continuous Transformers
- AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Use
- A Probabilistic Framework for LLM-Based Model Discovery
- A Progressive Evidence Localization Framework Based on Wasserstein Gradient Flows for Document Visual Question Answering
- A Provable Expressiveness Hierarchy in Hybrid Linear-Full Attention
- A proximal ADMM for multiblock problems with block anti-upper triangular constraints
- A Pure Hierarchical Spectral Parcellation Network for Brain Network Analysis
- A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
- A Random Matrix Perspective on the Consistency of Diffusion Models
- A Random Matrix Theory of Masked Self-Supervised Learning
- Arboreal Neural Network
- ArborKV: Structure-Aware KV Cache Management for Scaling Tree-based LLM Reasoning
- ArcDAE: Asymmetric Rectified Contrastive Diffusion Autoencoder for Unified Representation Learning
- ARC-Decode: Accelerated Decoding with Risk-Bounded Acceptance
- Architecture Matters for Multi-Agent Security
- ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin
- AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
- AReaL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models
- A recipe for scalable attention-based ML potentials: unlocking long-range accuracy with all-to-all node attention
- Are Common Substructures Transferable? Understanding Transferability in Graph Pretraining under Riemannian Geometry
- A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
- A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning
- Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach
- A Regime-Aware Trajectory Prediction Framework for 1000+ Systems Biology Models
- A Regret Minimization Framework on Preference Learning in Large Language Models
- Are Large Reasoning Models Interruptible?
- Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations
- ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking
- Are Object-Centric Representations Better At Compositional Generalization?
- Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning
- Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination
- Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?
- Are Your Agents Upward Deceivers?
- Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses
- A Risk Decomposition Framework for Pre-hoc Fine-tuning Prediction
- ARLArena: Demystifying Policy Gradient Stability in Agentic Reinforcement Learning
- A Robust Optimization Guided Pruning Framework for Vision and Large Language Models
- A robust PPG foundation model using multimodal physiological supervision
- Around the World in Eighty Ratings? Quantifying the Salience of Geo-Cultural Values for Pluralistic Alignment
- Artemis: Structured Visual Reasoning for Perception Policy Learning
- Artificial Hippocampus Networks for Efficient Long-Context Modeling
- ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization
- A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
- A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
- A Single Layer to Explain Them All: Understanding Massive Values in Large Language Models
- ASIR: Steganography for Diffusion Models via Antipodal Sampling and Iterative Recovery
- A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
- Ask Less, See More: Communication-Conditioned Token Pruning for Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models
- A Solvable High-Dimensional Model Where Nonlinear Autoencoders Learn Structure Invisible to PCA While Test Loss Misaligns With Generalization
- A Solver-Free Training Method for Predict-then-Optimize
- A Spiking Heterogeneous Harmonic Resonate-and-Fire State Space Model for Time Series
- ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models
- Assistive Prompt Mediation: Evaluating Language Models Under Accessibility Constraints
- A Statistical Framework for Analyzing Specification Resistance to Learnware-Inversion Risks
- ASTRA: Communication-Efficient Acceleration for Multi-Device Transformer Inference
- A Strictly Proper Scoring Rule and a Calibration Metric for Interval-Censored Data Analysis
- A Stronger Benchmark for Online Bilateral Trade: From Fixed Prices to Distributions
- A Studentized Spherical Harmonics–Based Nonparametric Two-Sample Test for Compositional and Directional Data
- Asymmetric conformal prediction with penalized kernel sum-of-squares
- Asymmetric Contrastive Objectives for Efficient Phenotypic Screening
- Asymmetric Multi-View Clustering with Hyperbolic Uncertainty Modeling
- Asymmetric Perturbation in Solving Bilinear Saddle-Point Optimization
- Asymmetric Prompt Weighting for Reinforcement Learning with Verifiable Rewards
- ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark
- Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
- Asymptotically Optimal Sequential Testing with Markovian Data
- Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy
- Asymptotic Theory of Iterated Empirical Risk Minimization, with Applications to Active Learning
- Asymptotic Universal Alignment: A New Alignment Framework via Test-Time Scaling
- AsyncSpade: Efficient Test-Time Scaling with Asynchronous Sparse Decoding
- A Systematic Study of Behavioral Cloning for Scientific Data Annotation
- A Tale of Two Graphs: Separating Knowledge Exploration from Outline Structure for Open-Ended Deep Research
- A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
- A Task-centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula
- AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters
- A Theoretical Framework for Modular Learning of Robust Generative Models
- A Theoretical Framework for Statistical Evaluability of Generative Models
- A Theoretical Game of Attacks via Compositional Skills
- A Theory of Contrastive Learning with Natural Images
- A Theory of Data Acquisition and Pricing at Scale
- A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
- A theory of learning data statistics in diffusion models, from easy to hard
- A Tight Theory of Error Feedback Algorithms in Distributed Optimization
- A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
- ATLAS: Learning to Optimally Memorize the Context at Test Time
- AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Material Structures
- Attacking Gray-Box Large Vision-Language Models with Adaptive SVD-Structured Adversarial Alignment
- Attacks on Machine-Text Detectors Retain Stylistic Fingerprints
- Attend to Anything: Foundation Model for Unified Human Attention Modeling
- Attention Hijacking: Backdooring Text Dataset Distillation via Semantic Anchors
- Attention Illuminates LLM Reasoning: The Uncovered Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization
- Attention Implements the Fisher Geometry of Exponential Families
- Attention Projection Mixing with Exogenous Anchors
- Attention's forward pass and Frank-Wolfe
- Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
- Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
- Attention Sinks in Diffusion Transformers: A Causal Analysis
- Attention with Routed-Memory for Learnable Sparse Control
- Attentive Multi-Layer Fusion for Vision Transformers
- At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
- Attn-QAT: 4-Bit Attention With Quantization-Aware Training
- Attributed Network Alignment: Statistical Limits and Efficient Algorithm
- Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
- A Two-Layer Framework for Joint Online Configuration Selection and Admission Control
- A Two-Tier Perspective on Inference-Time Parallelism in Multi-Agent LLM Systems
- AudioChat: Unified Audio Storytelling, Editing, and Understanding with Transfusion Forcing
- AudioMosaic: Contrastive Masked Audio Representation Learning
- Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributions
- AugMask: Score-Based Generative Modeling of Incomplete Tabular Data via Augmentation and Masking
- AugServe: Adaptive Request Scheduling for Augmented Large Language Model Inference Serving
- A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions
- A Unified Density Operator View of Flow Control and Merging
- A Unified Framework for Deep Hypergraph Clustering Beyond Homophily
- A Unified Framework for Diffusion Model Unlearning with f-Divergence
- A Unified Sparse Attention via Multi-Granularity Compression
- A unified theory of feature learning in RNNs and DNNs
- A Unifying Relational Perspective on Expressive Lottery Tickets
- A Unifying View of Variational Generative Wasserstein Flows
- AURA: Visually Interpretable Affective Understanding via Robust Archetypes
- AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning
- AutoBaxBuilder: Bootstrapping Code Security Benchmarking
- Autobidding Auctions with LLM-Powered Creatives
- AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation
- Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
- Automated Formal Proofs of Combinatorial Identities via Wilf–Zeilberger Guidance and LLMs
- Automatically Finding Reward Model Biases
- Automatic Layer Selection for Hallucination Detection
- Automatic Pruning Discovery for Large Language Models
- Automatic Unsupervised Ensemble Outlier Model Selection
- AutoMat: Physics-Guided Agentic Reasoning for Solving Ill-Posed Inverse Microscopy Problems
- AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture -of-Transformers for End-to-End Autonomous Driving
- AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design
- AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions
- AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
- AutoRAS: Learning Robust Agentic Systems with Primitive Representations
- Autoregression with Self-Token Prediction
- Autoregressive Boltzmann Generators
- Autoregressive Direct Preference Optimization
- Autoregressive Image Generation with Masked Bit Modeling
- Auto-regressive In-context Demonstration Selection
- Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction
- Autoregressive, Yet Revisable: In Decoding Revision for Secure Code Generation
- AutoRPA: Efficient GUI Automation through LLM-Driven Code Synthesis from Interactions
- AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents
- AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
- AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic
- AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines
- AvAtar: Learning to Align via Active Optimal Transport
- A Very Big Video Reasoning Suite
- AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation
- AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs
- Avoid What You Know: Divergent Trajectory Balance for GFlowNets
- AVTrack: Audio-Visual Speaker Tracking in Complex Scenes
- Awakening Visual Reasoning: Mitigating Post-Training Failure in Vision-Text Compression
- A World in Pieces: Structural Certification of General Agents
- Axiomatic Atlas: A Prescriptive Framework for Neural Architecture Design
- BabyVision: Visual Reasoning Beyond Language
- Backjump-on-Graph: Empowering LLMs with Reinforced Retrospective Exploration for Agentic KG Reasoning
- Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
- Backward SDE–Based Diffusion for Physics-Constrained Generation
- Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning
- Baguan-TS: dual in-context learning model for time series forecasting with covariates
- Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
- Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective
- Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
- Balancing plasticity and stability with Fast and Slow Successor Features
- Balancing Understanding and Generation in Discrete Diffusion Models
- BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
- Bandit Social Leaning Dynamics with Exploration Episodes
- BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning
- BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate
- Barriers to Counterfactual Credit Attribution for Autoregressive Models
- BAS: Bridging Adam and SignSGD for Memory-Efficient LLM Training
- Base Models Know How to Reason, Thinking Models Learn When
- BAT: Better Audio Transformer Guided by Convex Gated Probing
- Batched Contextual Reinforcement
- Batched First-Order Methods for Parallel LP Solving in MIP
- Batch Normalization for Neural Networks on Complex Domains
- Bayesian Gated Non-Negative Contrastive Learning
- Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
- Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
- Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements
- Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
- Bayesian Tensor Decomposition with Diffusion Model Prior
- Bayes-inspired Integration of Pretrained Priors and Few-Shot Evidence for Few-Shot Classification
- BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference
- BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps
- (Be Cautious!) Bio-Foundation Models Are Not Yet Robust to Biologically Plausible Perturbations and ML Transformations
- BEDTime: A Unified Benchmark for Automatically Describing Time Series
- Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
- Being More Lightweight and Practical: Mini-sized Contrastive Learning Pre-trained Models for Fine-grained Traffic Task
- Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering
- Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs
- Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
- Benchmarking and Enhancing VLM for Compressed Image Understanding
- Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation
- Benchmarking at the Edge of Comprehension
- Benchmarking Dense and Indiscernible Object Counting with Blueberries
- Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting
- Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
- Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis
- Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
- Benchmarking the Scientific Mind: Toward Evaluation of Complex-Reasoning Biomedical VQA
- Benchmarking World-Model Learning with Environment-Level Queries
- Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration
- Benign Overfitting in Adversarial Training for Vision Transformers
- BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
- BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback
- BEST: Benchmarking Efficiency in Space and Time for LLM-Generated Code
- Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
- Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching
- Better, Faster: Harnessing Self-Improvement in Large Reasoning Models
- Betting on Equilibrium: Monitoring Strategic Behavior in Multi-Agent Systems
- Betting on Predictions
- Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression
- Beyond Accuracy: Latent Perturbations for Cognitive-Aware Diagnosis
- Beyond Additive Decompositions: Interpretability Through Separability
- Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgery
- Beyond Benchmarks: Toward Causally Faithful Evaluation of Large Language Models
- Beyond Binary: Continuous State Optimization with Graph-Structured Objectives
- Beyond Blind Noising: Disentangled Visual Rectification for Hallucination Mitigation in MLLMs
- Beyond Buffer Limits: Energy-Based Data Reassembly for Continual Learning
- Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models
- Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
- Beyond Correctness: Distance-Based Social Dynamics of Multi-Agent Debate
- Beyond Description: Federated Adaptation via Semantic-Visual Prototype Alignment
- Beyond Detection: A Structure-Aware Framework for Scene Text Tracking
- Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-supervised Learning
- Beyond Drift: Stabilizing Subjective LLM Evaluation with Information-Theoretic Rubrics
- Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization
- Beyond Euclidean Summaries: Online Change Point Detection for Distribution-Valued Data
- Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs
- Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring
- Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
- Beyond First-order Asymptotics in Sequential Mean Testing
- Beyond Fixed Biases: Decoding the Role of Reasoning Uncertainty in MLLM Modality Conflicts
- Beyond Gemini-3-Pro: Revisiting LLM Routing and Aggregation at Scale
- Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion
- Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning
- Beyond Hamming: Query-Aware Decoding of Binary Cosine Sketches
- Beyond Heuristics: Learnable Density Control for 3D Gaussian Splatting
- Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering
- Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction
- Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
- Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC
- Beyond Logits: Coherent Hallucination Mitigation via Attention Contrastive Decoding
- Beyond Logits: Metastable Latent Dynamics for Sample-Efficient Best-of-N Selection in LLMs
- Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum
- Beyond Looking Up, Try Looking Around: Harmonizing Global Structure and Local Consistency in Optimal Transport for Short Text Clustering
- Beyond Magnitude: Scale-Invariant Evidential Fusion for Multi-View Classification
- Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
- Beyond Majority Voting: Self-Reflective Test-Time Reinforcement Learning for LLM Reasoning
- Beyond Mode Collapse: Distribution Matching for Diverse Reasoning
- Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
- Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting
- Beyond Next-Token Alignment: Distilling Multimodal Large Language Models via Token Interactions
- Beyond Normalization: Rethinking the Partition Function as a Difficulty Scheduler for RLVR
- Beyond Perplexity: UTF-8 Validity in Byte-aware Language Models
- Beyond Pixel Context Windows: Neural World Simulators with Persistent 3D State
- Beyond Pixels: Mining Compressed Domain Artifacts for Efficient AI-Generated Video Detection
- Beyond Point Predictions: Manifold Expansion and Dual Alignment for Robust Time Series Distillation
- Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning
- Beyond Policy Training: Recursive Solution Search from Unannotated Videos
- Beyond Prediction: Tail-Aware Scheduling for LLM Inference
- Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive Programming
- Beyond Procedure: Substantive Fairness in Conformal Prediction
- Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
- Beyond Reactivity: Proactive Adaptive Conformal Inference for Online LLM Factuality
- Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
- Beyond Rewards in RL for Cyber Defence
- Beyond Sample-Level Forgetting: Improving Reliability in Multimodal Unlearning
- Beyond Scalar Rewards: Learning from Text Feedback in LLM Post-Training
- Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability
- Beyond Single Embedding: Modeling User Preferences as Distribution in Federated Recommendation
- Beyond Single-View Indexing: Structure-Aware Multi-View Retrieval for Knowledge-Based VQA
- Beyond Soft Labels: Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression
- Beyond Softmax: A Natural Parameterization for Categorical Random Variables
- Beyond Static Allocation: Dynamic Sensitivity-Aware Fine-Tuning for Vision Transformers
- Beyond Static Endpoints: Tool Programs as an Interface for Flexible Agentic Web Services
- Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL
- Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability
- Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
- Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion
- Beyond Test-Time Training: Learning to Reason via Hardware-Efficient Optimal Control
- Beyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?
- Beyond the Bellman Recursion: A Pontryagin-Guided Framework for Non-Exponential Discounting
- Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
- Beyond Theorem Proving: Formulation, Framework and Benchmark for Formal Problem-Solving
- Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training
- Beyond the Trade-off: Unifying Fairness and Performance in Federated Learning
- Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
- Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
- Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning
- Beyond Unidirectional Bias: Reciprocal Perspective Calibration in Scene Graph Generation
- Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling
- BFTS: Thompson Sampling with Bayesian Additive Regression Trees
- Bi-Anchor Interpolation Solver for Accelerating Generative Modeling
- Biased Generalization in Diffusion Models
- Biases in the Blind Spot: Detecting What LLMs Fail to Mention
- Bias in Zeroth-Order Normal Estimation for Decision-Based Attacks
- Bias-Spectrum Neural Processes for Parametric PDEs: Architecture Priors Meet PDE Constraints
- BiCrossNet with Decoupled Dual Generators: A Parameter‑Efficient and Generalizable Few‑Shot Custom Gesture Recognition Framework
- Bilevel Optimization over Saddle Points of Zero-Sum Markov Games
- Bilinear Bandits with Partially Observable Features
- Bimodal masked language modeling for bulk RNA-seq and DNA methylation representation learning
- Bioacoustic Geolocation: Species Sounds as Geographic Signals
- BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics
- BIOARC: Discovering Optimal Neural Architectures for Biological Foundation Models
- BioDynaSpec: Harmonic-Guided Spatio-Spectral Autoregressive Diffusion for Protein Dynamics Generation
- BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series
- Bio-Inspired Self-Supervised Learning for Wrist-worn IMU Signals
- Biologically plausible heavy-tailed connectivity enhances generalizations on cognitive tasks in recurrent neural networks
- BioToken and BioFM – Biologically-Informed Tokenization Enables Accurate and Efficient Genomic Foundation Models
- Bio-Vision-Inspired Spiking Neural Networks for Object Detection with Event Cameras
- Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
- BiRQA: Bidirectional Robust Quality Assessment for Images
- BIT-LLM: Brain Instruction Tuned LLM with persistent Cross-Attention for fMRI-to-Text Decoding
- BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning
- Bits That Count: Quantifying and Predicting Capabilities of Language Models
- BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation
- Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
- Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning
- Black-Box Detection of LLM-Generated Text Using Generalized Jensen Shannon Divergence
- Blending Neural Control Density Functions for Stabilization and Safety
- Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
- BLIPs: Bayesian Learned Interatomic Potentials
- BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining
- BLOCK-EM: Preventing Emergent Misalignment via Latent Blocking
- Blocking the Leakage: Manifold-Aware Gradient Projection for Long-Horizon Test-Time Adaptation
- Block Rotation is All You Need for MXFP4 Quantization
- Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking
- BlueCodeAgent: A Blue Teaming Agent Powered by Automated Red Teaming for CodeGen AI
- BOCLOAK: Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
- BOOSTAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models
- Boosting CVaR Policy Optimization with Quantile Gradients
- Boosting Monocular Metric Depth Estimation via Bokeh Rendering
- Boosting Video Diffusion Models via Masked Autoencoders as Tokenizers
- Boosting World Models Learning via Latent-Space Value Alignment
- Boost the Identity-Preserving Embedding for Consistent Visual Generation
- Bootstrapped Exploration with Causal Reasoning: A Training Paradigm for Adaptive Forecasting Agent
- Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
- Bottleneck Communication Delay Minimization for Communication-Efficient Decentralized Learning
- Bottleneck-Guided Spectral Subgoals For Offline Goal-Conditioned RL
- Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
- Bounded Hyperbolic Tangent: A Stable and Efficient Alternative to Pre-Layer Normalization in Large Language Models
- Box Thirding: Anytime Best Arm Identification under Insufficient Sampling
- BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
- BPL: Generalizable Deepfake Detection via Bias-only Pair-aware Learning
- BrainJanus: A Foundation Model for Unified Understanding and Generation across Brain, Vision, and Language
- Branching Diffusion for Point Processes in Time and Space
- Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
- Breaking Dual Bottlenecks: Evolving Unified Multimodal Models into Self-Adaptive Interleaved Visual Reasoners
- Breaking Manifold Continuity: Vector Quantized Modeling for Real-Centric Deepfake Detection
- Breaking Multi-Task Curse: Reward-Weighted Evolution for Black-Box Many-Task Optimization
- Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models
- Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
- Breaking the Computational Barrier: Provably Efficient Actor–Critic for Low-Rank MDPs
- Breaking the Echo Chamber: A Dynamic Ensemble Pruning Perspective on MoE
- Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
- Breaking the Factorization Barrier in Diffusion Language Models
- Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation
- Breaking the Reference Bottleneck via Learning to Rewrite Conversational Queries without Gold Reference Passages
- Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge
- Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
- Breaking the Self-Confirming Loop: Diagnosing and Mitigating Systemic Reward Bias in Self-Rewarding RL
- Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
- Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning
- Break the Block: Dynamic-size Reasoning Blocks for Diffusion Large Language Models via Monotonic Entropy Descent with Reinforcement Learning
- Bregman meets Lévy: Stochastic Mirror Descent with Heavy-Tailed Noise in Continuous and Discrete Time
- Brep2Shape: Boundary and Shape Representation Alignment via Self-supervised Transformers
- BRIDGE: Predicting Human Task Completion Time From Model Performance
- BRIDGE: Triangular Fixed-Point Refinement for Long-Horizon Persona Consistency
- Bridging Dynamics and Data: A Unified Diffusion Framework for Mechanistically-Informed Epidemic Forecasting
- Bridging Functional and Representational Similarity via Usable Information
- Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation
- Bridging Local–Global Dissonance: Learning from Compressive Measurements for Hyperspectral Reconstruction
- Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training
- Bridging RGB and RAW: Single-step Deterministic Flow with Homogeneous Aligned Guidance
- Bridging Scaling Laws to On-Policy Reinforcement Learning via Adaptive Batch Scaling
- Bridging Spherical Black-Box Optimizers
- Bridging Structure and Semantics: Uncertainty-Modulated Dual-Path Diffusion for Robust Text-Attributed Graph Learning
- Bridging the Gap Between Average and Discounted TD Learning
- Bridging the Gap in Autonomous Science: The Corpus and Benchmark for Biological Protocol Reasoning
- Bridging the Grounding Gap in VideoQA via Typed Memory for Language-based Belief-State Reasoning
- Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
- Bridging the Perceptual Gap: Residual-Enhanced Downscaling and Manifold-Aware Perception Alignment Adaptation for NR-IQA
- Bridging the Stability-Expressivity Gap: Synthetic Data Scaling and Preference Alignment for Low-Resource Spoken Language Models
- Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
- Bridging Tokens and Geometry: Token-wise 3D Supervision for CAD Generation
- Bridging Your Imagination with Audio-Video Generation via a Unified Director
- Bring Future Vision: Dynamic Computation Allocation Guided by Lightweight Feature Forecaster
- Bringing Code ALIVE: Optimizing Interactive Frontend Mini-Games via Automated Play and Reinforcement Learning at Scale
- Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting
- Broadening the Backdoor Basin: Understanding LLM Backdoors Collapse and Making Backdoors Persistent
- BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs
- BroRL: Scaling Reinforcement Learning via Broadened Exploration
- B-Spar: Bayesian Sparse-Reward Modeling for RL-based Image Editing
- BTSP-CAM: A Brain-Inspired Geometric Memory for Class-Incremental Learning
- BubbleSpec: Turning Long-Tail Bubbles into Speculative Rollout Drafts for Synchronous Reinforcement Learning
- Budget-Constrained Step-Leve Diffusion Caching
- Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
- Budget-Efficient Attacks and Robustness Training for Cooperative MARL
- Budget-Feasible Mechanisms for Submodular Welfare Maximization in Procurement Auctions
- BuildArena: A Physics‑Aligned Interactive Benchmark of LLMs for Engineering Construction
- Building Better Deception Probes Using Targeted Instruction Pairs
- Building Reliable Long-Form Generation via Hallucination Rejection Sampling
- Building Social World Model with Large Language Models
- Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination
- Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks
- Butterworth as Attention: Anisotropic Spectral Gating for Pansharpening
- BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception
- BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
- Byte Pair Encoding for Efficient Time Series Forecasting
- C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
- Cache Coherent Resampling for Efficient Test Time Scaling in LLM Reasoning via Adaptive Sequential Monte Carlo
- CacheEdit: Efficient Multi-round Image Editing via Adaptive Token-wise Reuse.
- CACR: Reinforcing Temporal Answer Grounding in Instructional Video via Candidate-Aware Causal Reasoning
- CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization
- CAffNet: Hard Constraint-Affine Neural Networks
- Calibrated Knowledge Aggregation in Bayesian Mixture-of-Experts for Continual VQA
- Calibrated Multimodal Representation Learning with Missing Modalities
- Calibrated Preference Learning: The Case of Label Ranking
- Calibrated Test-Time Guidance for Bayesian Inference
- Calibrating Conservatism for Scalable Oversight
- Calibrating Decision Robustness via Inverse Conformal Risk Control
- Calibrating Generative Models to Distributional Constraints
- Calibrating Uncertainty for Zero-Shot Adversarial CLIP
- CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction
- CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling
- CalPro: Prior-Aware Evidential Conformal Prediction with Structure-Aware Sensitivity Bounds for Protein Structures
- CAMEL: Confidence-Gated Reflection for Reward Modeling
- CamGeo: Sparse Camera-Conditioned Image-to-Video Generation with 3D Geometry Priors
- CAMP: Coherent Alignment of Multimodal Prototypes for Explainable Complementary Learning
- Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad
- Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
- Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
- CANDI: Hybrid Discrete-Continuous Diffusion Models
- Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models
- Can Large Language Models Generalize Procedures Across Representations?
- Can LLM Agents Stick to the Script? Modeling Commitment in Interactive Narratives
- Can LLMs Reason Like Automated Theorem Provers for Rust Verification? VCoT-Bench: Evaluating via Verification Chain of Thought
- Can LLMs Reason Structurally? Benchmarking via the lens of Data Structures
- Can local learning match self-supervised backpropagation?
- Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
- Can Muon Fine-tune Adam-Pretrained Models?
- Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
- Can Simple Denoising Improve Uniform State Diffusion Models?
- Can vision language models learn intuitive physics from interaction?
- Can VLMs Diagnose and Recover from VLA Manipulation Faults?
- Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction
- CAOS: Conformal Aggregation of One-Shot Predictors
- Capability-Oriented Training Induced Alignment Risk
- Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms
- Capacity-Agnostic Parameter Isolation for Continual Graph Learning
- Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization
- Capacity without Access: Reinterpreting the Mid-Depth Spectral Plateau in LLMs
- CAPTCHA Solving for Native GUI Agents: Automated Reasoning-Action Data Generation and Self-Corrective Training
- Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- Caracal: Causal Architecture via Spectral Mixing
- CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation
- Cardinality-Invariant Neural Operator Policies for Scalable PDE Control
- Cardio-mmFlow: A Gaussian-Prior-Free Physics-Informed Flow Matching Framework for Electrocardiogram to mmWave Radar Synthesis.
- CARE: Adaptive Calibration for Reliable Recommendations
- CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
- CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
- CAReDiO: Enhancing Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization
- Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features
- CAST: Modeling Visual State Transitions for Consistent Video Retrieval
- CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments
- Catch-22: On the Fundamental Tradeoff Between Detectability and Robustness in LLM Watermarking
- Categorical Flow Maps
- Categorical Reparameterization with Denoising Diffusion models
- CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching
- CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
- CauchyNet: Compact and Data-Efficient Learning using Holomorphic Activation Functions
- Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
- CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution
- Causal Attention with Lookahead Keys
- Causal-aware Anomaly Detection for Tabular Data
- Causal Dependency-Aware Unsupervised Routing for Large Reasoning Models
- Causal Detection of Multi-Step LLM Agent Attacks
- Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
- Causal Discovery for Irregularly Time Series with Consistency Guarantees
- Causal discovery for time series with endogenous context variables
- Causal Disentangled Anchor Learning for Scalable Fair Multi-view Clustering
- Causal Effect Identifiability in the Presence of Latent Confounders Without Auxiliary Variables
- Causal-EPIG: Causally Aligned Active CATE Estimation
- Causal Feature Learning via Generalized Rayleigh Quotients
- Causal Fine-Tuning under Latent Confounded Shift
- Causal Flow Q-Learning for Robust Offline Reinforcement Learning
- Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Video Generation
- CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
- Causal Identification from Counterfactual Data: Completeness and Bounding Results
- Causal Inference with Transformer Models
- Causal-JEPA: Learning World Models through Object-Level Latent Interventions
- Causally Evaluating the Learnability of Formal Language Tasks
- Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
- Causal Modeling of Selection in Evolution
- Causal Preference Elicitation
- CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
- Causal Representation Learning with Optimal Compression and Complex Treatments
- Causal Structure Learning for Sparse Matrix Fill-in Reduction
- CausalX: A Unified and Causally-Interpretable Plug-and-Play Model for Multi-modal Spatio-Temporal Forecasting
- CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning
- CauScale: Neural Causal Discovery at Scale
- CauSciBench: Evaluating LLM Causal Inference for Scientific Research
- CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception
- Causes and Consequences of Representational Similarity in Machine Learning Models
- CB-SLICE: Concept-Based Interpretable Error Slice Discovery
- CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models
- CCLRec: Consensus-driven Contrastive Learning for LLM-enhanced Graph Recommendation
- CE$^4$L: Continual Ego, Exo, and Ego-Exo Learning
- CELL: A Causal Perspective for Fairness-aware Graph Adaptation
- CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
- Cello: A Universal Cell-wise Feature Aggregation framework for Reliable Pathology Images Analysis
- Censoring with Plausible Deniability: Asymmetric Local Privacy for Multi-Category CDF Estimation
- CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding
- Cerebellar-Inspired Residual Control for Fault Recovery: From Inference-Time Adaptation to Structural Consolidation
- Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE
- Certificate-Guided Pruning for Stochastic Lipschitz Optimization
- Certificates for Complex-Compatible Learned Cochain Laplacians
- Certified Circuits: Stability Guarantees for Mechanistic Circuits
- Certified Robustness under Heterogeneous Perturbations via Hybrid Randomized Smoothing
- Certifying Capabilities from Finite Tests: When Is It Possible?
- Certifying Graph Neural Networks Against Label and Structure Poisoning
- Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing
- CFPO : Counterfactual Policy Optimization For Multimodal Reasoning
- CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models
- CGRiC: Compositional Risk Certification for Structured LLM Outputs
- CGSVD: Cascaded Granular Singular Value Decomposition for Large Language Model Compression
- Chain-of-Glimpse: Search-Guided Progressive Object-Grounded Reasoning for Video Understanding
- Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
- Chain-of-Thought Gradient Descent
- Chain-of-Thought Reasoning In The Wild Is Not Always Faithful
- Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
- Channel Adapter for Time Series Foundation Models in Zero-Shot Multivariate Forecasting
- ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awareness
- Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
- Characterizing Agents in Production
- Characterizing, Evaluating, and Optimizing Complex Reasoning
- Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling
- Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment
- ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing
- Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
- CHB: A Diagnostic Toolkit for Hardness-Aware Clustering Evaluation
- Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics - Rotating Detonation Engines
- Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-dimensional Control Tasks
- CHESS: Chebyshev Spectral Synthesis for Trajectory Condensation
- Chiral Symmetry Breaking in Transformers: A Group-Equivariant Framework for Solving the Reversal Curse via Adjoint Manifold Mappings
- Chunk-Guided Q-Learning
- CIRBench: Evaluating Large Language Models as LLVM IR Optimizers
- Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Vision-Language Models
- CircuitPrint: Mechanistic Circuit Fingerprints for Large Language Models
- CiteGuard: Conformal False-Discovery Control for Faithful Retrieval-Augmented Generation
- CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
- CLAM-Bench: Benchmarking LLM Agents for Library-Scale Cross-Architecture Migration
- Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation
- CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
- CLASP: Online learning algorithms for Convex Losses And Squared Penalties
- Class-Conditional Distribution Balancing for Group Robust Classification
- Class-Grouped-Normalized-Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
- Class-Prior Perturbation-Robust Regularization for Imbalanced Unreliable Partial Label Learning
- CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Subtitle Removal
- CL-GCL: Comprehensive and Lightweight Graph Contrastive Learning
- ClimateAR: Multi-Scale Autoregressive Generative Modeling for Seasonal-to-Interannual Climate Forecasting
- CLIMB: Taming the LoRA Residency Cliff in Multi-LoRA Serving
- CLINIC : Evaluating Multilingual Trustworthiness in Language Models for Healthcare
- CLINIC: Towards High-quality Graph Out-Of-Distribution Detection
- ClinTutor-R1: Advancing Scalable and Robust One-to-Many Alignment in Clinical Socratic Education
- Clipped Q-Learning: Your Value Clipping Is Secretly A Robust Operator
- Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals
- Clipping Low-Probability Tokens in SFT Yields a Generalizable Initialization for RL
- Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
- CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large Vision-Language Models
- Closing the Expression Gap in LLM Instructions via Socratic Questioning
- Closing the Loop: Universal Repository Representation with RPG-Encoder
- Closing the Sim-to-Real Gap in Non-Markovian Spreading Processes via GPU-Accelerated Distributional RL
- CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
- Clover: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
- [CLS] is Not Enough: Multi-Label Recognition via Patch-Level Inference and Adaptive Aggregation
- Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series
- Clustered Influence Functions
- Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning
- Clustering in Deep Stochastic Transformers
- CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction
- cMoLLM at Scale: Horizontal Scaling Laws for Convolutionally-Gated Mixture-of-LLMs
- CoarseBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations
- Coarse-Grained Boltzmann Generators
- COBRA: Contribution-Based Bayesian Rank Allocation for Parameter-Efficient Fine-Tuning
- CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning
- CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering
- CoCoQuant: Breaking the Bandwidth Wall via Co-Optimized Communication and Computation Quantization
- CoCoReviewBench: A Completeness- and Correctness-Oriented Benchmark for AI Reviewers
- CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
- CoDA-Bench: Can Code Agents Handle Data-Intensive Tasks?
- Code2Video: A Code-centric Paradigm for Educational Video Creation
- Code2Worlds: Empowering Coding LLMs for 4D World Generation
- CodeChemist: Test-Time Scaling for Low-Resource Code Generation via Functional Knowledge Transfer
- CodeClash: Benchmarking Goal-Oriented Software Engineering
- CodeMamba: Shifting from Target Semantics to Self-Supervised Background Manifold Learning for Singularity Detection in Infrared Sequences
- CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
- CODiff: One-Step Diffusion Model for Camouflaged Object Detection
- CoEvol-NO: State and Coordinate Co-Evolution with an Error-Driven Predictor-Corrector Paradigm for Neural Operator Transformer
- Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner
- Co-Evolving Latent Action World Models
- CofactGVR: Counterfactual Intervention for Grounded Visual Reasoning
- CoFrGeNet: Continued Fraction Architectures for Language Generation
- CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation
- COFT: Counterfactual–Conformal Decoding for Fair Chain‑of‑Thought Reasoning in Large Language Models
- CoGenCast: A Coupled Autoregressive–Flow Generative Framework for Time Series Forecasting
- Co-Generative De Novo Functional Protein Design
- CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection
- Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement
- COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing
- CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving
- CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
- Cold-Start Personalization via Training-Free Priors from Structured World Models
- CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement
- Collaborative and Efficient Fine-tuning: Leveraging Task Similarity
- Collaborative Disagreement Resolution for Scalable Oversight
- Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation
- Collaborative Threshold Watermarking
- COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space
- Colorful Pinball: Density-Weighted Quantile Regression for Conditional Guarantee of Conformal Prediction
- Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution
- Column Thresholding for Sparse Spiked Wigner Models: Improved Signal Strength Requirements
- CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models
- Combinatorial Sparse PCA Beyond the Spiked Identity Model
- Combining Theory and Benchmarks: Towards A Virtuous Cycle to Understand and Guarantee Foundation Model Performance
- CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning
- CoMem: Context Management with A Decoupled Long-Context Model
- Commit to the Bit: Reactive Reinforcement Learning Done Right
- Compact Conformal Subgraphs
- Compass-RoPE: Isotropic Rotary Position Embeddings for Vision Transformers
- Comp-Attn: Present-and-Align Attention for Compositional Video Genneration
- Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs
- CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
- Complexity bounds for Dirichlet process slice samplers
- Complexity of Decentralized Optimization with Mixed Affine Constraints
- ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
- Component-Wise Composite Likelihood Distillation for Censored Time-to-Event Data
- Compositional Behavioral Semantics and Metrics for State Abstraction in Reinforcement Learning
- Compositional Generative Modeling from Decentralized Data
- Compositional Perception and Generalizing Induction: Latent Compositional Manifold Assumption on Generalized Category Discovery
- Compositional Planning with Jumpy World Models
- Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning
- Compressed Sensing for Capability Localization in Large Language Models
- Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
- Computational Arbitrage in AI Model Markets
- Computationally-efficient Graph Modeling with Refined Graph Random Features
- Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
- Compute When Worth It: Risk Control for Reasoning on a Compute Budget
- Computing Provable Bounds for Exact Shapley Values of Neural Networks
- Concept Concentration for Faithful Representation Intervention
- Concept-Guided Tokenization: Closing the Gap Between Reconstruction and Generation
- Concept Heterogeneity-aware Representation Steering
- ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation
- Concept Removal for Frontier Image Generative Models
- Conditional Clifford-Steerable CNNs for PDE Modeling
- Conditional Coverage Diagnostics for Conformal Prediction
- Conditional Diffusion Sampling
- Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
- Conditional Equivalence of DPO and RLHF: Assumptions, Failure Modes, and Provable Alignment
- Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
- Conditionally Site-Independent Neural Evolution of Antibody Sequences
- Conditional Quantile Adjusted Conformal Prediction for Time Series
- Condition-Aware Graph Flow Matching for Modeling the Distributions of Complex Physical Systems
- Condition Number Based Low-Bit Quantization for Image Super-Resolution
- ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution
- Conf-Gen: Conformal Uncertainty Quantification for Generative Models
- Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning
- Confidence is Not Universal: Task-Dependent Calibration and Emergent Behavior in LLMs
- Configurable Reward Model for Balanced Safety Alignment
- Conflict-Aware Adaptive Alignment for LLM Hallucination Mitigation
- Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
- Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
- ConFlux: Multivariate Time Series in Flux, One Unified Forecast in Confluence
- Conformal Calibration Transfer
- Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
- Conformal Policy Control
- Conformal Prediction for Early Stopping in Mixed Integer Optimization
- Conformal Reliability: A New Evaluation Metric for Conditional Generation
- Conformal Risk-Averse Decision Making with Action Conditional Guarantee
- ConFu: Contemplate the Future for Better Speculative Sampling
- CONGA:Confidence-and-Gradient-Aware Learning Rate Schedule for Test Time Adaptation
- Connecting Independently Trained Modes via Layer-Wise Connectivity
- ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure
- Conservation Laws for Modern Neural Architectures
- ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving
- Consistency Deep Equilibrium Models
- Consistency Training Is Not Neutral to Alignment
- Consistent Diffusion Language Models
- Consistent Zero-Shot Imitation with Contrastive Goal Inference
- ConsMSA: Semantic Distribution Consistency Learning for Multimodal Sentiment Analysis
- Constitutional Black-Box Monitoring for Scheming in LLM Agents
- Constrained Adaptive Rejection Sampling
- Constrained Bayesian Experimental Design via Online Planning
- Constrained Flow Optimization via Sequential Fine-Tuning for Molecular Design
- Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
- Constrained Meta Reinforcement Learning with Provable Test-Time Safety
- Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
- Constructing Industrial-Scale Optimization Modeling Benchmark
- Content-Style Identification via Differential Independence
- Context-Aware Reaonser : Enhancing Contextual Reasoning in Multimodal Large Language Models
- Context Distillation Retains Post-Training Capabilities in Continually Trained LMs
- Context-Driven Incremental Compression for Multi-Turn Dialogue Generation
- Context Forcing: Consistent Autoregressive Video Generation with Long Context
- Context-free Recognition with Transformers
- Context-level Language Modeling by Learning Predictive Context Embeddings
- CONTEXTOR: Contextualized High-order Contrastive Learning
- Context Tuning for In-Context Optimization
- Contextualized Privacy Defense for LLM Agents
- Contextualized Visual Personalization in Vision-Language Models
- Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
- Contextual Slate GLM Bandits with Limited Adaptivity
- Continual Adaptation at Scale: Towards Sustainable AI
- Continual GUI Agents
- Continual Learning of Domain-Invariant Representations
- Continual Learning through Control Minimization
- Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe
- Continual Model Routing in Evolving Model Hubs
- Continual Segmentation under Joint Nonstationarity
- Continuity-Regularized Flow Matching for Offline Reinforcement Learning
- Continuous Diffusion Models Can Obey Formal Syntax
- Continuous-Time Piecewise-Linear Recurrent Neural Networks
- Continuous Variable Hamiltonian Learning at Heisenberg Limit via Displacement-Random Unitary Transformation
- Continuous Viewpoint Adaptation for Single View 3D Object Reconstruction
- CONTINUUM: Restoring the Contiguous Tensor Abstraction Efficiently for Dynamic AI Workloads via Hardware Virtualization
- Contractive Anchor Resolvent Diffusion for Incomplete Multi-View Clustering
- ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
- Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
- Contrastive Flow Map Matching
- Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design
- Contrastive Order Learning: A General Framework for Ordinal Regression
- Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations
- Contrastive Representation Regularization for Vision-Language-Action Models
- Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP
- Contrastive Symbolic Regression: Aligned Representations, Adaptive Prediction, and Diverse Ensembles
- Contrastive Weak-to-Strong Generalization
- Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
- Control Consistency Losses for Diffusion Bridges
- Controllable and explainable personality sliders for LLMs at inference time
- Controllable Molecule Generation via Sparse Representation Editing: An Interpretability-Driven Perspective
- Controlled Collaboration Geometry for Personalized Federated Learning
- Controlled Dynamics Attractor Transformer
- Controlled LLM Training on Spectral Sphere
- Controlled SDEs for Long-Horizon Motion Generation under Latent Decision Uncertainty
- Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
- ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
- Convergence Analysis of Decentralized Hessian-/Jacobian-Free Algorithm for Nonconvex Stochastic Bilevel Optimization
- Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings
- Convergence of Two-Timescale Stochastic Approximation with Markovian Samples and Applications in Reinforcement Learning
- Convergence Rate Analysis of the AdamW-Style Shampoo: Unifying One-sided and Two-Sided Preconditioning
- Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
- Convergent World Representations and Divergent Tasks
- Conversation for Non-verifiable Learning: Self-Evolving Large Language Models through Meta-Evaluation
- Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
- ConvexBench: Can LLMs Recognize Convex Functions?
- Convex Dataset Valuation for Post-Training
- Convex Distance Operator Transport: Convex and Geometry-Preserving Information
- Convex Low-resource Accent-Robust Language Detection in Speech Recognition
- Convex Optimization for Alignment and Preference Learning on a Single GPU
- Convolutional Learnable-Group Weightless Neural Network
- Cooperative variance estimation and Bayesian neural networks disentangle aleatoric and epistemic uncertainties
- CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas
- Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations
- CooT: Learning to Coordinate In-Context with Coordination Transformers
- CoPE: A Framework for Optimizing Coordination between Planning and Execution in LLM-based Agents
- CoPE: Continual Probe-guided Expansion for Large Vision-Language Models
- COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
- Copula-SVI: Vine-Copula Variational Inference for Instance-Level Correlation Capturing
- Copyright-Bench: Agentic Evaluation of Copyright Law Compliance
- CORAL: Uncertainty-Aware Regulation of Exposure Concentration in Recommender Systems
- CoRe: Collaborative Reasoning via Cross Teaching
- CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learning
- CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
- CoRe: Context-Robust Remasking for Diffusion Language Models
- Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents
- CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
- CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems
- Corrected Samplers for Discrete Flow Models
- Correcting in Hindsight: Editing Past Key-Value States for Robust LLM Reasoning
- Correcting Overparameterization Effects in Fair Empirical Risk Minimization
- Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference
- Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
- CorrectionPlanner: Self-Correction Planner with Reinforcement Learning in Autonomous Driving
- Correct looks better: Pairwise comparisons reveal accuracy rankings
- Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
- Correspondence Cognitive Learning for Multi-Modal Object Re-Identification
- Corrigibility Transformation: Constructing Goals That Accept Updates
- CorrSteer: Generation-Time LLM Steering via Correlated Sparse Autoencoder Features
- Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
- Cost-aware Stopping for Bayesian Optimization
- CoT is Not the Chain of Truth: An Empirical Internal Analysis of Reasoning LLMs for Fake News Generation
- Counterfactual Bootstrap for Robust Meta-Reinforcement Learning
- Counterfactual Occlusion-Aware Learning via Visibility Intervention for LiDAR Anomaly Detection
- Counterfactual Residual Data Augmentation for Regression
- CountsDiff: A diffusion model on the natural numbers for generation and imputation of count-based data
- Coupled Cluster con MoLe: Molecular Orbital Learning for Neural Wavefunctions
- Coupled Training with Privileged Features and Unlabeled Data
- Coupled Trigger Optimization and Vulnerable Parameter Alignment for Persistent Backdoor Attacks on Federated Learning
- Coupled Variational Reinforcement Learning for Language Model General Reasoning
- Courtroom Analogy: New Perspective on Uncertainty-Aware Classification
- Covariance estimation using Markov chain Monte Carlo
- Covariance Volume Maximization for Embodied Latent Exploration in Deep Reinforcement Learning
- Coverage ≠ Exposure: Auditable Control of Same-Support Tail Failures under Multimodal Missingness
- Coverage Improvement and Fast Convergence of On-policy Preference Learning
- Coverage, Not Averages: Semantic Stratification for Trustworthy Retrieval Evaluation
- CoverPruneGS: Coverage-Preserving Structured Pruning for Hierarchical 3D Gaussian Splatting from Sparse-View Monocular Videos
- CPMöbius: Iterative Coach–Player Reasoning for Data-Free Reinforcement Learning
- CRAG: Can 3D Generative Models Help 3D Assembly?
- CRAMER: Control via Request-Aware Masking for Editing Recommenders
- Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
- Creat3r: Confidence Reaggregation for Exploration-aware Active 3D Reconstruction
- Credibility-Aware Weighting Federated Causal Discovery for Time Series
- Credible Information Subset Decomposition: An End-to-End Multi-fidelity Learning Model by Modeling Label Information
- Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking
- Credit Assignment via Neural Manifold Noise Correlation
- CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
- Crisp: A Spectral-Based Interaction Strategy for Multivariate Time Series Forecasting
- CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing
- Criterion-Conditional In-Context Learning: Evaluating Criterion-Shift Adaptation in Vision-Language Models
- Critique-Guided Distillation for Robust Reasoning via Refinement
- Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
- Cross-Embodiment Robot Foundation World Models with Latent Actions
- Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundations and Algorithms
- Cross-Modal Semantic Decoupling and Transfer for Text-to-Visible-Infrared Person Re-Identification
- CrossQ: Task-Aligned Cross-Token Conditional Quantization for Late Interaction Retrieval
- Cross-Subject Modeling for Widefield Calcium Imaging via Atlas-Aligned Spatiotemporal Tokenization
- Cross-Tactile Sensor Representation Learning
- Cross-task Calibration for Asynchronous Federated Continual Learning
- Cross-View Lewis Weight Fusion Empowering Exemplar Replay for Federated Class-Incremental Learning
- Crowd4D: Scene-Aware Monocular 4D Crowd Reconstruction
- CRPO: Character-centric Group Relative Policy Optimization for Role-aware Reasoning in Role-playing Agents
- CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM
- CSD: Content-aware Speculative Decoding for Efficient Image Generation
- CSG: Cognitive Structure Generation for Intelligent Education
- CSOR: Coreset Selection for Object Re-identification via Class Pruning
- CSPLoRA: Confidence-Guided Structure Planning for Low-Rank Adaptation
- CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning
- CUARewardBench: Benchmark for Evaluating Reward Models on Computer-using Agent Trajectories
- Culture x AI: Evaluating AI as a Cultural Technology
- CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLM
- Curated Synthetic Data Doesn’t Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
- Curating the Future: A Scalable Recipe for Training Open-Ended Forecasters
- CURE: Consistency-under-Unified Semantic Regularization for Generalized Category Discovery
- CURE: Context-driven Diffusion with Progressive Expansion for Single Domain Generalization in Time Series Classification
- cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transport
- Cure-SFT: Diagnostic-Guided Data Curation for Instruction Tuning
- Curriculum-Guided Layer Scaling for Language Model Pretraining
- Curriculum Reinforcement Learning for Black-Box Prompt Tuning via Large Language Models
- CURVE: Learning Causality-Inspired Invariant Representations for Robust Scene Understanding via Uncertainty-Guided Regularization
- CurvZO: Adaptive Curvature-Guided Sparse Zeroth-Order Optimization for Efficient LLM Fine-Tuning
- Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity
- CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability
- CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception
- CyberCycle: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
- CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict
- Cycle-of-Science: Reliable Reasoning through Counterfactual Verification for Agent Decision Making
- D$^2$O: A Dual Debiasing Operator for Training-Free Test-Time Adaptation of Vision–Language Models
- D$^3$: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training
- D²Evo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning
- d2: Improved Techniques for Training Reasoning Diffusion Language Models
- d2p: Fast and Scalable Structured Attention with Differentiable Dynamic Programming
- d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation
- DADP: Domain Adaptive Diffusion Policy
- DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
- DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
- DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
- DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits
- DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
- DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding
- D-ARL: A Distribution-Matched Asynchronous Reinforcement Learning Framework for Language Reasoning
- DART: Distribution-Aware Adaptive Relational Transfer for Adversarial Attacks against Closed-Source MLLMs
- DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
- Darwinian Memory: A Training-Free Self-Regulating Memory System for GUI Agent Evolution
- DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers
- Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
- Data- and Variance-dependent Regret Bounds for Online Tabular MDPs
- Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise
- Data Difficulty and the Generalization–Extrapolation Tradeoff in LLM Fine-Tuning
- Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching
- DataGuard: A Non-intrusive Dataset Auditing Framework via Differential Information Forensics
- Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique
- Data Reconstruction: Identifiability and Optimization with Sample Splitting
- Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories
- Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks
- Data-Source Adaptive Online Learning under Heteroscedastic Noise
- DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
- daVinci-Dev: Agent-native Mid-training for Software Engineering
- DB-KSVD: Scalable Alternating Optimization for Disentangling High-Dimensional Embedding Spaces
- DC-LA: Difference-of-Convex Langevin Algorithm
- DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding
- D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
- DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning
- DDGA: Dirichlet Distributional Gradient Aggregation for Transferable Vision-Language Adversarial Attacks
- DDIM Inversion as a Perturbation Amplifier: Breaking Mimicry Protection via Reconstruction Error Minimization
- DDP-WM: Disentangled Dynamics Prediction for Efficient World Models
- DDSVM: A Differentiable Framework for Deep Support Vector Machines with Iterative Geometry-Aware Optimization
- De4D-SLAM: Gradient-Isolated Static-Dynamic Decoupling for Monocular SLAM in Dynamic Environments
- De-attribute to Forget for LLM Unlearning
- Debate2Create: Robot Co-design via Multi-Agent LLM Debate
- Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models
- Debiased Model-based Representations for Sample-efficient Continuous Control
- DecAEvolve: Decompose, Adapt, and Evolve, or, Three Pillars of Effective LLM-based Scientific Equation Discovery
- Decentralized and Disentangled Task–Role Representation Learning for Generalizable Offline Multi-Agent Meta Reinforcement Learning
- Decentralized Bandits without Global Clock for Dynamic Matching Market
- Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging
- Decentralized Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower Bounds
- DecepChain: Inducing Deceptive Reasoning in Large Language Models
- DecFus: Decentralized Layer-wise Fusion with Dynamic Exploration and Exploitation
- Decision-Focused Learning via Tangent-Space Projection of Prediction Error
- Decision-focused Sparse Tangent Portfolio Optimization
- Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning
- Decision Transformers As Zero-Shot Learners via Text-Behavior Alignment
- Decision Tree Learning on Product Spaces
- DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
- DeCoDe: Decoupling Binding Position and Molecular Conformation in 3D Ligand Diffusion for Structure-Based Drug Design
- DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions
- DecodeShare: Tracing the Shared Pathways of LLM Decode-Time Decisions
- Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
- DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation
- Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding
- Decompose, Structure, and Repair: A Neuro-Symbolic Framework for Autoformalization via Operator Trees
- Decomposing Out-of-Distribution Error in Conditional Flow Matching via Wasserstein Geometry
- Decomposing Query-Key Feature Interactions Using Contrastive Covariances
- Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
- Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling
- DECOR: Learning to Decompose and Collaborate in Deep Search via Multi-Agent Reinforcement Learning
- Decouple and Cache: KV Cache Construction for Streaming Video Understanding
- Decoupled Low-Rank Adaptation for Robust Federated Fine-Tuning
- Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
- Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training
- Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
- Decoupling Regularization and Privacy in Differentially Private Ridge Regression and ERM
- Decoupling Skeleton and Flesh: Efficient Multimodal Table Reasoning with Disentangled Alignment and Structure-aware Guidance
- Decoupling The "What" and "Where" With Polar Coordinate Positional Embedding
- Decoupling Universal Laws and Environmental Heterogeneity: A Physics-Inspired Framework for Robust Spatio-Temporal Forecasting
- Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization
- DecoVer: A Decompose-and-Verify Neuro-Symbolic Framework for Embodied Task Planning with BC+
- Decoy for the Judge: Disrupting Multi-Turn Jailbreaks using Semantics-Preserving Output Rewriting
- DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
- DeepBlip: Estimating Conditional Average Treatment Effects Over Time
- Deep Coupling Learning for Solving PDEs
- Deep Discriminative Structure Proxy Hashing for Cross-modal Retrieval
- Deep Ensemble Clustering for Visual Representation Learning
- Deep Flow Networks
- Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression
- DeepHA: Scaling Action Chains Elicits Deep Hierarchical Agents
- DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories
- Deep Incentive Design with Differentiable Equilibrium Blocks
- Deep Learning for Bioimaging: What are we actually learning?
- Deep Learning for Code: Towards Human-Centered Coding Agents
- Deep Multi-view Graph Clustering via Attribute-aware Bidirectional Structural Refinement and Pseudo-label Guided Multi-level Fusion
- Deep Networks Learn Deep Hierarchical Models
- Deep networks learn to parse uniform-depth context-free languages from local statistics
- Deep Neural Network Regression with Functional Covariates
- Deep neural networks divide and conquer dihedral multiplication
- Deep Pre-Alignment for VLMs
- Deep Progressive Training: scaling up depth capacity of zero/one-layer models
- Deep Reinforcement Learning Finds Bayes-Nash Equilibrium in Competitive Newsvendor Problems
- Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models
- Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction for Electronic Density of States
- Deep sequence models tend to memorize geometrically; it is unclear why.
- DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
- Deep Single-Index Fréchet Regression
- Deep Trajectory Supervision: Deep Supervision Strikes Back
- DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation
- DeFacto: Counterfactual Thinking with Images for Enforcing Evidence-Grounded and Faithful Reasoning
- Deformba: Vision State Space Model with Adaptive State Fusion
- DEGAP: Dynamic Entropy-Guided Attention Perturbation for Contrastive Decoding in Large Vision-Language Models
- Degradation-Aware Metric Prompting for Hyperspectral Image Restoration
- Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
- Delegation and Verification under AI
- Deliberate Evolution for Sample-Efficient Symbolic Regression with LLM
- De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution
- DELTA4: Sparse Matrix-Vector Multiplication for Low Sparsity
- DeltaEvolve: Accelerating Scientific Discovery through Momentum-Driven Evolution
- Delving into Muon and Beyond: Deep Analysis and Extensions
- Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
- Demystifying Action Space Design for Robotic Manipulation Policies
- Demystifying Entropy Control in LLM RL Training: Theoretical Analysis and Dynamic Scheduling
- Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling
- Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success
- Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
- Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
- Demystifying the Optimal Fair Classifier in Multi-Class Classification
- Demystifying When Pruning Works via Representation Hierarchies
- Denoising without Diffusion: Fixed-Noise Denoiser Anomaly Detection in Tabular Data
- Dense associative memory for Gaussian distributions
- DenseMLLM: Standard Multimodal LLMs are Intrinsic Dense Predictors
- DenseSteer: Steering Small Language Models towards Dense Math Reasoning
- Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs
- Density-Guided Continuous Flow for Robust Counterfactual Explanations
- Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models
- Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs
- Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
- Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies
- Depth-Progressive Monotonic Learning without Backpropagation
- Derivative Informed Learning of Exchange-Correlation Functionals
- Deriving Neural Scaling Laws from the Statistics of Natural Language
- Designing noise schedules for diffusion models with spectral analysis
- Designing Observation and Action Models for Efficient Reinforcement Learning with LLMs
- Design Linear Constrained Neural Layers with Implicit Convex Optimization
- Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning
- Detached Skip-Links and $R$-Probe: Decoupling Feature Aggregation from Gradient Propagation for MLLM OCR
- DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
- Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised Representation
- Detecting Contextual Hallucinations in Large Language Models with Frequency-Aware Attention
- Detecting Errors in AI-Generated Annotations: When and Why Semantic Neighbors Help
- Detecting Fluent Optimization Based Adversarial Prompts via Sequential Entropy Changes
- Detecting Perspective Shifts in Multi-Agent Systems
- Detecting the Semantic Fixed Point: A Geometric Framework for Efficient Inference
- Deterministic Component Mining for Multi-framework UI2Code Generation
- Deterministic Differentiable Structured Pruning for Large Language Models
- Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatch
- DevEvol: Benchmarking LLM Agents on Continuous Software Evolution
- DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
- DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
- DFlash: Block Diffusion for Flash Speculative Decoding
- DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers
- DFSAttn: Dynamic Fine-grained Sparse Attention for Efficient Video Generation
- D-FUSEr: Diverse Failure, Unified Success via Error-Distribution Shaping in LLM Reasoning
- DGG-HMR: Multi-Person Human Mesh Recovery with Depth-Guided Geometric Anchoring
- dgMARK: Decoding-Guided Watermarking for Diffusion Language Models
- DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection
- Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers
- Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
- Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory
- Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
- DiasR: Dual-Modal Identity-Anchored Sparse Routing for Efficient Multi-Subject Video Generation
- Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations
- Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descent
- Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning
- DiffCrossGait: Trajectory-Level Alignment for 2D-3D Cross-Modal Gait Recognition via Latent Diffusion
- Diffeomorphism-Equivariant Neural Networks
- Difference-Aware Decision Learning for Multimodal Image Fusion
- Differentiable Conformal Training for LLM Reasoning Factuality
- Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning
- Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Control
- Differentially Private Continual Release with Relative Error
- Differentially Private Cross-Silo Recommendation from Implicit Feedback
- Differentially Private Geodesic Regression
- Differentially Private Preference Data Synthesis for Large Language Model Alignment
- Differentially Private Range Subgraph Counting
- Differentially Private Submodular Maximization with a Knapsack Constraint
- Differentially Private Synthetic Tabular Data via Private Evolution
- Differential Smoothing Mitigates Sharpening and Improves LLM Reasoning
- Differential syntactic and semantic encoding in LLMs
- Different Usage of Shared Components Explains Behavioral Variance in LLMs
- Diffract: Spectral View of LLM Domain Adaptation
- DiffStyle3D: Consistent 3D Gaussian Stylization via Attention Optimization
- DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
- DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
- DiffuReason: Enhancing Reasoning Ability for Diffusion Language Models via Monte Carlo Tree Search
- Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection
- Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies
- Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement
- Diffusion Bridge or Flow Matching? A Unifying Framework and Comparative Analysis
- Diffusion differentiable resampling
- Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees
- Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
- Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
- DiLA: Disentangled Latent Action World Models
- DiL: Discrete-anchored Representation Alignment for Semi-Supervised Continual Learning
- Dimensional Collapse in Transformer Attention Outputs: A Challenge for Sparse Dictionary Learning
- Dimensionality Reduction with Point-distributions Similarity Invariant
- Dimension-free convergence of diffusion models for approximate Gaussian mixtures
- Dimension-Free Multimodal Sampling via Preconditioned Annealed Langevin Dynamics
- Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
- DiP-G: Discrete Prompting for Graph Neural Networks
- Direct 3D-Aware Object Insertion via Decomposed Visual Proxies
- DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
- Direct Flow Q-Learning
- Directional Neural Collapse for Self-Supervised Visual Representation Learning
- Directly Optimizing Natural Language Explanations for Behavioral Faithfulness: Simulatability and Recoverability
- Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs
- DiscoForcing: A Unified Framework for Real-Time Audio-Driven Character Control with Diffusion Forcing
- DiScoFormer: Plug-In Density and Score Estimation with Transformers
- DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation
- Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring
- Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
- Discovering Differences in Strategic Behavior between Humans and LLMs
- Discovering Implicit Large Language Model Alignment Objectives
- Discovering Interpretable Algorithms by Decompiling Transformers to RASP
- Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
- Discovering Scaling Exponents with Physics-Informed Müntz-Szász Networks
- Discovering Symmetry Groups with Flow Matching
- DiscoverLLM: From Executing Intents to Discovering Them
- Discrete Adjoint Schrödinger Bridge Sampler
- Discrete Diffusion Samplers and Bridges: Off-Policy Algorithms and Applications in Latent Spaces
- Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies
- Discrete Diffusion with Physical Mass Constraints for \emph{De Novo} Peptide Sequencing
- Discretely-Refined Multi-view Clustering via Aligned Anchor Learning
- Discrete Survival Knowledge Distillation for Competing Risks Analysis
- Discrete Tilt Matching
- Discretized Density-Guided Source-Free Adaptation for Continuous Targets
- Discriminative Attribute Graph Clustering Through Topology-Guided Contrastive Learning
- Discriminative Mixture-of-Experts on Graphs with Reliable Expert Fusion
- Discriminative Visual Process Rewards for Scaling Thinking at Test-Time with Images
- Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography
- Disentangling a Large Language Model’s Computation from its Chain-of-Thought
- Disentangling Consensus and Value-Specific Representations for Controllable Pluralistic Value Alignment of LLMs
- Disentangling Geometry, Performance, and Training in Language Models
- Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment
- Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
- Disentangling meaning from language in LLM-based machine translation
- DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
- Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding
- Dismantling the Illusion of Vision-Language-Action Models Competence via Explicit Distributional Shifts
- Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models
- DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation
- DisPPO: Quantile-Based Distributional Reinforcement Learning for Large Language Models
- Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection
- Dissecting Causal Mechanism Shifts via FANS: Function And Noise Separation
- Dissecting Embodied Abilities in Multimodal Language Models through Skill-level Evaluation and Diagnosis
- Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers
- Dissecting Post-Training: Uncovering the Complementary Roles of SFT and RL for Document Parsing
- Dissecting Quantization Error: A Concentration-Alignment Perspective
- Dissecting the Safety Circuit: Neuronal Intervention for Transferable Adversarial Attacks on VLMs
- DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction
- DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
- Distillation Models are Good Samplers for Diffusion Reinforcement Learning
- Distilling Geometry Priors for 3D-Consistent Video Generation
- Distilling Linearized Behavior into Non-linear Fine-Tuning for Effective Task Arithmetic
- Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs
- Distilling Task-Level Coordination Policies for Generalizable Multi-Agent Cooperation
- Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
- Distinguishing Imitation Error from Intrinsic Motion Learning Difficulty
- DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction
- Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner
- Distributed Direct Preference Optimization
- Distributed Stochastic $K$-Level Optimization Over Networks
- Distributional Active Inference
- Distributional Alignment Games for Answer-Level Fine-Tuning
- Distribution Alignment for One-Shot Federated Learning via Optimal Transport
- Distributional Inverse Reinforcement Learning
- Distributionally Robust Causal Abstractions
- Distributionally Robust Markov Games with Average Reward
- Distributionally Robust Reinforcement Learning with Human Feedback
- Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
- Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
- Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge
- Distribution Matching Variational AutoEncoder
- Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
- DITING: A Weak Degradation Listener for Battery Lifetime Early Prediction
- DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs
- DIVA: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement
- Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
- DIVER: Diving Deeper into Distilled Data via Expressive Semantic Recovery
- Divergence Decoding: Targeted Unlearning via Auxiliary Models
- Diversity-Aware Recursive Feature Multiple Kernel Learning
- Diversity-aware Weight Perturbation Promotes Robust Adaptation
- Diversity-Driven Offline Multi-Objective Optimization via Bi-Level Pareto Set Learning
- Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models
- Diversity Over Frequency: Rethinking Tool Use in Visual Chain-of-Thought Agents
- Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
- DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use
- Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
- Divide and Contrast: Learning Robust Temporal Features without Augmentation
- Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
- Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
- Diving into Kronecker Adapters: Component Design Matters
- Divisiveness-Consistent Label Distribution Learning
- DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
- DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model
- dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching
- DLLMQuant: A Post-Training Quantization Framework Tailored for Diffusion-Based Large Language Models
- DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders
- DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making
- DLO-Lab: Benchmarking Deformable Linear Object Manipulations with Differentiable Physics
- DMCO: Budget-Aware Co-Optimization of Data Cleaning and AutoML
- DNACHUNKER: Learnable Tokenization for DNA Language Models
- dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning
- DNA: Uncovering Universal Latent Forgery Knowledge
- Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox
- DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents
- DOCKSMITH: Scaling Reliable Coding Environments via an Agentic Docker Builder
- DocOS: A Benchmark for Proactive Document-Guided Actions in GUI Agents
- Doc-to-LoRA: Learning to Instantly Internalize Contexts
- DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
- “Do Diffusion Models Dream of Electric Planes?” Discrete and Continuous Simulation-Based Inference for Aircraft Design
- Does a Hybrid Space-Aware Randomized Defense Improve Empirical and Certified Adversarial Robustness?
- Does AI Reviewer See the Full Picture? Attacking and Defending Multimodal Peer Review
- Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
- Does Reasoning Improve Seeing? Understanding When Vision-Language Models Benefit from Thinking
- Does Reinforcement Fine-Tuning Improve Generalization of LLM Agents? An Empirical Study
- Does Your Reasoning Model Implicitly Know When to Stop Thinking?
- Do Language Models Track Entities Across State Changes?
- Do LLMs “Feel”? Emotion Circuits Discovery and Control
- Do LLMs Signal When They’re Right? Evidence from Neuron Agreement
- Domain Adaptation with Adaptive $f$-Divergence: Tighter Variational Representation and Generalization Bounds
- Domain Adaptive Object Detection via Dynamic Causal Refinement
- Domain Restriction via SAE Multi-Layer Transitions
- Domain-Shift-Aware Conformal Prediction for Large Language Models
- Domain Transfer Becomes Identifiable via a Single Alignment
- DomED: Redesigning Ensemble Distillation for Domain Generalization
- Do Natural Language Interpretability Methods Convey Privileged Information?
- Do Neural Operators Forget Geometry? The Forgetting Hypothesis in Deep Operator Learning
- Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
- Don't Force the Fit: Bounded Log-Likelihood Loss for Enhanced Reasoning in Large Language Models
- Don't Forget Why You Started: Tackling Dual Forgetting in Vision-Language Continual Learning
- Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
- Don't Overthink with Pixels: Efficient Reasoning for Segmentation
- Don't Reinvent the Wheel, Just Realign the Spokes: Resource-Efficient Federated Fine-Tuning via Rank-Wise Expert Assembly
- Don't Walk the Line: Boundary Guidance for Filtered Generation
- Doppler Prompting for Stable mmWave-based Human Pose Estimation
- Do-Prompt: Causal Interventions Meet Variational Prompt Bottlenecks
- Do Text Edits Generalize to Visual Generation? Benchmarking Cross-Modal Knowledge Editing in UMMs
- DOT-MoE: Differentiable Optimal Transport for MoEfication
- Do Transformers Need Three Projections? Systematic Study of QKV Variants
- (Doubly) Exponential Lower Bounds for Follow the Regularized Leader in Potential Games
- Doubly Outlier-Robust Online Infinite Hidden Markov Model
- Doubly Regularized Markov Decision Processes for Robust Reinforcement Learning
- Doubly Robust Distributionally Robust Offline Contextual Pricing
- DOUBT: Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness for Hallucination Detection in MLLMs
- Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models
- Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
- Do You Want to Know if Two Distributions Are Close to Each Other?Testing the Closeness With Statistical Significance
- DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
- DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and Its Loss' Convexity is Dispensable)
- DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole Slide Image Survival Prediction
- DR$^2$Seg: Decomposed Two-Stage Rollouts for Efficient Reasoning Segmentation in Multimodal Large Language Models
- Draft-and-Audit Reinforcement Learning for Optimization Modeling
- Draft-Conditioned Constrained Decoding for Structured Generation in LLMs
- DREAM: A Unified Framework for Drift-Corrected Federated Multi-Objective Learning
- DreamDojo: A Real-Time Robot World Model from Large-Scale Human Videos
- DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise
- DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation
- Dreaming in Code for Curriculum Learning in Open-Ended Worlds
- Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
- DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution
- DRFusion: Drift-Resilient Temporally Consistent Infrared–Visible Video Fusion
- DRIFT-BENCH: Diagnosing CoopeRative Breakdowns in LLM Agents under Input Faults via Multi-Turn Interaction
- DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
- Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning
- DRIVE: Best Data Scheduling Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation
- DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation
- DriveWorld-VLA: Unified Latent-Space World Modeling with Vision–Language–Action for Autonomous Driving
- Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
- DRL-STAF: A DRL Framework for State-aware Forecasting of Complex Multivariate Hidden Markov Process
- DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents
- DroneDINO: Towards Heterogeneous Routed Mixture of Experts for Drone-based Unified Object Detection
- Drop-in Circulant Structural Priors for Transformer Decoding of Cyclic Codes
- DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting
- Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
- DRPBench: Evaluating LLMs in Concurrent Code Comprehension via Fine-grained Data Race Prediction
- DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs
- DSENet: A Novel Dual-Stream Enhancement Network for Multi-Scale Non-Stationary Time Series Forecasting
- DSGCR: Decomposed Spectral Geometry-Aware Cross-Modal Semantic Representation for 3D Visual Grounding
- DSGym: A Standardized and Holistic Framework for Advancing Data Science Agents
- DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA
- DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
- dTRPO : Trajectory Reduction in Policy Optimization of Diffusion Large Language Models
- DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
- Dual-branch Robust Unlearnable Examples
- Dual-Calibration Multi-View Clustering via Compact Anchor Learning
- Dual-channel Dynamic Graph Neural Networks with Adaptive Adjacency Learning and Multi-scale Representation Fusion
- DualCOIL: Offline Imitation Learning from Contrasting Demonstrations
- Dual Latent Memory for Visual Multi-agent System
- Dual-Latent Memory Routing for Vision-Language Reasoning
- Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models
- Dual Optimal Transport for Multi-Concept Composition: Structural Alignment and Texture Injection in Diffusion Models
- DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models
- Dual Quaternion SE(3) Synchronization with Recovery Guarantees
- Dual-stage Contrastive Learning-enhanced Multi-view Variational Clustering
- Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model
- DualTimesField: Rethinking Time Series as Continuous-Time Trends and Events
- Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy
- DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing
- DuRP: Dual-Stage Physics-Embedded Learning for Joint Radiance and Polarization Restoration
- Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding
- DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
- DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
- DyGRO-VLA: Cross-Task Scaling of Vision–Language–Action Models via Dynamic Grouped Residual Optimization
- DyLLM: Efficient Diffusion LLM inference via saliency-based token selection and partial attention
- DynaMem: Consistent Long Video Generation via Hierarchical Memory and Motion Priors
- Dynamic Compression Flows for Neuroscience Data
- Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases
- Dynamic Fractal Mamba: A Neural Renormalization Group Flow for Scale-Invariant Sequence Modeling
- Dynamic High-Dimensional Facility Location with Low Recourse
- Dynamic Linear Attention
- Dynamic Multimodal Evaluation via Knowledge-Enhanced Benchmark Evolution
- Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents
- Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes
- Dynamic Regret via Discounted-to-Dynamic Reduction with Applications to Curved Losses and Adam Optimizer
- Dynamic Relational Priming Improves Transformer in Multivariate Time Series
- Dynamics and representation structure of local approximations to gradient-based learning in linear recurrent neural networks
- Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation
- Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues
- Dynamics Reveals Structure: Challenging the Linear Propagation Assumption
- Dynamic Stratified Contrastive Learning with Upstream Augmentation for MILP Branching
- Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure
- Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training
- Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
- Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
- DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents
- DynaTok: Token-Based 4D Reconstruction from Partial Point Clouds
- DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving
- Dyn-VPP: Video Prediction Policy Optimization for Improved Visual Dynamics
- DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion
- Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
- E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory
- E²I-VRWKV: Explicit EPI-Representation and Interaction-Aware Vision-RWKV for Light Field Semantic Segmentation
- EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
- EAKV: An Entropy-Driven Adaptive KV Compression Framework for Long Video Understanding
- EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance
- EARL: Towards a Unified Analysis-Guided Reinforcement Learning Framework for Egocentric Interaction Reasoning and Pixel Grounding
- Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models
- Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
- EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference
- Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling
- ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation.
- ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization
- ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
- EchoAttention: Exploiting Token-Pair Redundancy and Frame-Block Similarity for Efficient Long Video Generation
- ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
- ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
- Echoes within the Reasoning: Stealth and Effective Watermarking via Chain of Thought
- EchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs
- EchoRL: Reinforcement Learning via Rollout Echoing
- ECO: Quantized Training without Full-Precision Master Weights
- EcoVLA: Environment-Aware Adaptive Pruning with Interleaved Inference Orchestration for Vision-Language-Action Models
- ECSEL: Explainable Classification via Signomial Equation Learning
- EDCO: Dynamic Curriculum Orchestration for Domain-specific Large Language Model Fine-tuning
- Edge-colored Clustering in Hypergraphs: A MaxECC Approximation
- Editable Proof Sketch for Automated Theorem Proving
- Edit-Based Refinement for Parallel Masked Diffusion Language Models
- EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation
- EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
- EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models
- EEmo-Logic: A Unified Dataset and Multi-Stage Framework for Comprehensive Image-Evoked Emotion Assessment
- Effective Model Pruning : Measuring the Redundancy of Model Components
- Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
- Effective Reasoning Chains Reduce Intrinsic Dimensionality
- effGen: Enabling Small Language Models as Capable Autonomous Agents
- Efficient Adaptive Testing via Gradient Path Matching Subset Selection for AI Education
- Efficient and Minimax-optimal In-context Nonparametric Regression with Transformers
- Efficient and Safe Molecular Assembly via Reinforcement Learning and Constraint Solving
- Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning
- Efficient Bayesian Inference from Noisy Pairwise Comparisons
- Efficient Bilevel Optimization for CKA-Guided MoE Upcycling
- Efficient Code Analysis via Graph-Guided Large Language Models
- Efficient Continuous-Depth Modeling with GRU Equivalents
- Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
- Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
- Efficient Diffusion Models via Time Step Optimization with Consistent Training and Inference Constraints
- Efficient Distributed MLLM Training with ModalGlue
- Efficient Distributionally Robust Assortment Optimization in MNL Bandits
- Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed
- Efficient DP-SGD for LLMs with Randomized Clipping
- Efficient Equivariant High-Order Crystal Tensor Prediction via Cartesian Local-Environment Many-Body Coupling
- Efficient Generative Modeling beyond Memoryless Diffusion via Adjoint Schrödinger Bridge Matching
- Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads
- Efficient Inference for Noisy LLM-as-a-Judge Evaluation
- Efficient Learned Image Compression without Entropy Coding
- Efficient Learning of Compositional Targets with Hierarchical Spectral Methods
- Efficient Learning of Deep State Space Models via Importance Smoothing
- Efficient LLM Moderation with Multi-Layer Latent Prototypes
- Efficiently Learning Drifting Halfspaces with Massart Noise
- Efficiently Solving Discounted MDPs via Predictions with Unknown Prediction Errors
- Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
- Efficient Mismatch-Tolerant Coding for Model-Driven Compression
- Efficient Multi-Agent Reasoning via Confidence-Guided Adaptive Debate
- Efficient Multi-modal Dataset Distillation via Analytic Parameter Matching
- Efficient Multimodal Question Answering
- Efficient Multi-round LLM Inference over Disaggregated Serving
- Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
- Efficient numeracy in language models through single-token number embeddings
- Efficient Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback
- Efficient Online Variational Estimation via Monte Carlo Sampling
- Efficient Parallel Samplers for Recurrent-Depth Models
- Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
- Efficient Preference Poisoning Attack on Offline RLHF
- Efficient privacy loss accounting for subsampling and random allocation
- Efficient, Property-Aligned Fan-Out Retrieval via RL-Amortized Diffusion
- Efficient Public Verification of Private ML via Regularization
- Efficient Rashomon Set Approximation for Decision Trees
- Efficient Reasoning with Hidden Thinking
- Efficient RL Training for LLMs with Experience Replay
- Efficient Skill Grounding via Code Refactoring with Small Language Models
- Efficient Stochastic Optimisation via Sequential Monte Carlo
- Efficient Synthetic Network Generation via Latent Embedding Reconstruction
- Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
- Efficient Test-time Inference for Generative Planning Models with OCL Search
- Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models
- Efficient Training-Free Multi-Token Prediction via Embedding-Space Probing
- Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization
- Efficient Transformer Attention for SNNs via Hadamard Simplification
- Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets
- EGG: An Expert-Guided Agent Framework for Kernel Generation
- Ego3S: Select, Strengthen, and Synchronize for Efficient Egocentric Reasoning
- EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
- EigenCache: Rethinking Diffusion Acceleration as Covariance-Optimal Forecasting and Submodular Information Allocation
- Eigenvectors of Experts are Training-free Non-collapsing Routers
- Ekka: Automated Diagnosis of Silent Errors in LLM Inference
- Elastic Attention: Test-time Adaptive Sparsity Ratios for Efficient Transformers
- Elastic Diffusion Transformer
- ElicitR: Unlocking Latent Reasoning in Dense Retrievers via Generative Regularization
- Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learned Force Fields
- Eliminating Solution Bias in Differentially Private Optimization
- Ellipsoidal Time Series Forecasting
- Embedding Hybrid Systems into Continuous Latent Vector Fields
- Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation
- EMBGUARD: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents
- Embodied-DETR: End-to-End Temporal 3D Object Detection in Egocentric Views
- Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models
- Embodied Task Planning via Graph-Informed Action Generation with Large Lanaguage Model
- Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
- EmBrace: A Collective Knowledge Fusion Framework Toward Unified EEG Foundation Models
- E-mem: Multi-Agent Based Episodic Context Reconstruction for LLM Agent Memory
- Emergence of Biased Consensus in Multi-Agent LLM Debates
- Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
- Emergence of Hierarchical Emotion Organization in Large Language Models
- Emergent Alignment via Competition
- Emergent Analogical Reasoning in Transformers
- Emergent Biological Realism in RL-Trained DNA Language Models
- Emergent Communication Under Misinformation
- Emergent Visual Representations through Unsupervised Spiking Networks with Synaptic Pruning
- EMFormer: Efficient Multi-Scale Transformer for Accumulative Context Weather Forecasting
- Empirical Gaussian Processes
- Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality
- EmWorld: Emotion World Model with Latent State Evolution for Scenario-Incremental Dynamic Facial Expression Recognition
- Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping
- Endogenous Resistance to Activation Steering in Language Models: Evidence for Internal Consistency Monitoring in Llama-3.3-70B
- End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
- End-to-End Compression for Tabular Foundation Models
- End-to-end Graph-structured Brain Representation Learning
- EnerGS: Energy-Based Gaussian Splatting under Partial Geometric Observability
- Energy-based Compositional Diffusion Planning
- Energy-Structured Low-Rank Adaptation for Continual Learning
- EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
- Enhanced Latent-Space Adversarial Training for Super-Resolution
- Enhanced Multi-Instance Partial Label Learning via Average Gradient Outer Product
- Enhancing Affine Maximizer Auctions with Correlation-Aware Payment
- Enhancing Conformal Prediction via Class Similarity
- Enhancing Cross-subject Emotion Recognition via Heterogeneous Distribution Augmentation and Collaborative Learning
- Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
- Enhancing LLM Training via Spectral Clipping
- Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
- Enhancing Multi-Modal LLMs Reasoning via Difficulty-Aware Group Normalization
- Enhancing Neural Theorem Proving via High-Quality Proof Selection and Verifier Feedback
- Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
- Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding
- Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization
- Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos
- EnsembleVLA: Ensemble Learning for Vision-Language Action Models
- Ensembling Sparse Autoencoders
- Entangled No More: Multi-Domain Decoupling for Robust Dynamic Graph Neural Networks
- EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
- EntRAG: Entity-Centric Retrieval-Augmented Generation for Knowledge-based Visual Question Answering
- EntroKV: Entropy-Guided Dynamic Budget Allocation for KV-Cache Compression
- Entropic Mirror Monte Carlo
- Entropy-Aware Dynamic KV Cache Sparsification for Autoregressive Image Generation and Editing
- Entropy-Aware On-Policy Distillation of Language Models
- Entropy-aware Span-Constrained Optimal Transport for Robust Cross-Tokenizer Knowledge Distillation
- Entropy-informed Decoding: Adaptive Information-Driven Branching
- Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
- Envy-Free Allocation of Indivisible Goods via Noisy Queries
- ePC: Fast and Deep Predictive Coding for Digital Hardware
- EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments
- EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance
- EpiCoCo: De Novo Epitope Generation via MHC-Context Co-Modeling and Contrastive Affinity Guidance
- Episodic Memory-Guided Controllable Experience Synthesis for Reinforcement Learning
- Epistemic Gain, Aleatoric Cost: Uncertainty Decomposition in Multi-Agent Debate for Math Reasoning
- Epistemic Uncertainty Quantification for Pre-trained VLMs via Riemannian Flow Matching
- EpiTwin: Spatiotemporal Graph Transformers for Epileptic sEEG Signal Reconstruction
- EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation
- EPSVec: Efficient and Private Synthetic Text Generation via Dataset Vectors
- EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D Partial Differential Equations
- Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models
- EquiCAD: A Geometric Equivariant Neural Network for 3D Shape Classification
- Equilibrium Pricing in Oligopolistic Data Markets
- Equilibrium Propagation for Non-Conservative Systems
- Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
- Equivalence of Context and Parameter Updates in Modern Transformer Blocks
- Equivariant Covariance Tensors: Guaranteed SPD Uncertainty for Tensor-Valued Geometric Learning
- Equivariant Latent Alignment via Flow Matching under Group Symmetries
- Equivariant Neural Networks for General Linear Symmetries on Lie Algebras
- ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs
- Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
- ERGeoBench: A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
- Error Amplification Limits ANN-to-SNN Conversion in Continuous Control
- Error Analysis of Discrete Flow with Generator Matching
- Error-Driven Graph Augmentation for Mesh-Based PDE Surrogates
- Error Propagation and Model Collapse in Diffusion Models: A Theoretical Study
- Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricing
- Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion Models
- Escaping Mode Collapse in LLM Generation
- Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
- Escaping the Likelihood Trap: Geometric Diversity Optimization for Long-Form Image Captioning
- Escaping the Mode: Multi-Answer Reinforcement Learning in LMs
- Escaping the Subspace Trap: The Role of Optimizer Geometry in Model Width Expansion
- Escaping the Verifier: Learning to Reason via Demonstrations
- Escaping Whack-a-Mole: Code Documentation Optimization via Dependency-Guided Bi-level Search
- Esoteric Language Models
- Estimating Correlation Clustering Cost in Node-Arrival Stream
- Estimating Tail Risks in Language Model Output Distributions
- Estimating the Empowerment of Language Model Agents
- Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
- ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment
- Euclean: Automated Geometry Problem Formalization with Unified Verification in Lean
- Euler–Poincaré Neural Dynamics: A Geometric-Mechanics Framework for Scientific Simulation
- E-VAds: An E-commerce Short Videos Understanding Benchmark for MLLMs
- Evaluating Agentic Optimization on Large Codebases
- Evaluating AI Grading on Real-World Handwritten College Mathematics: A Large-Scale Study Toward a Benchmark
- Evaluating and Explaining Prompt Sensitivity of LLMs Using Interactions
- Evaluating and Rewarding LALMs for Expressive Role-Play TTS via Mean Continuation Log-Probability
- Evaluating and Steering Modality Preferences in Multi-modal LLMs
- Evaluating bivariate causal statements based on mutual compatibility
- Evaluating Contextual Illegality: AI Compliance in Corporate Law Scenarios
- Evaluating Language Models in Realistic Conversational Contexts
- Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
- Evaluating LLM Uncertainty in Long-Form Generation Using Deterministic Ground Truth
- Evaluating Object-Centric Models beyond Object Discovery
- Evaluating Parameter Efficient Methods for RLVR
- Evaluating Robustness of Reasoning Models on Parameterized Logical Problems
- Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights
- Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
- Even Faster Kernel Matrix Linear Algebra via Density Estimation
- Event2Vec: Processing neuromorphic events directly by representations in vector space
- Every Step Counts: Decoding Trajectories as Authorship Fingerprints of dLLMs
- Evidential Copula Concept Embedding Models
- Evidential Reasoning Advances Interpretable Real-World Disease Screening
- EVMbench: Evaluating AI Agents on Smart Contract Security
- EvoC2F: Compiling Tool Orchestration for Efficient and Evolvable LLM Agents
- EvoCF: Multi-Agent Collaboration via Agentic Memory-Driven Evolutionary Counterfactual Planning
- EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design
- EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
- Evolutionary Generation of Multi-Agent Systems
- Evolutionary Multi-View Classification with Label Noise via Gradient and Feature Dual-Perception
- Evolution of Benchmark: Black-Box Optimization Benchmark Design through Large Language Model
- Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
- Evolution Strategies at the Hyperscale
- Evolving Interdependent Operators with Large Language Models for Multi-Objective Combinatorial Optimization
- Evolving Interpretable Constitutions for Multi-Agent Coordination
- Evolving Quantitative Reasoning through Self-Play in Digital Twin Markets
- EVOLVING ROLLOUTS: Harnessing Historical Experience for Web Agent Evolution in Reinforcement Learning
- EvoMAS: Heuristics in the Loop—Evolving Smarter Agentic Workflows
- EvReflection: Event-Driven Micro-Dynamics for Reflection Removal
- Exact and Approximate Algorithms for Polytree Learning
- Exact Functional ANOVA Decomposition for Categorical Inputs
- Exactly Computing do-Shapley Values
- Exact Unlearning in Reinforcement Learning
- Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
- ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation
- Executable Agentic Memory for GUI Agent
- ExpAlign: Expectation-Guided Vision–Language Alignment for Open-Vocabulary Grounding
- Expandable, Compressible, Mineable: Open-World Thermal Infrared Image Restoration
- Expanding the AI Evaluation Toolbox with Statistical Models
- Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
- Expand Neurons, Not Parameters
- Expectation Alignment of Language Models for Real-World User Expectations
- Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
- Expected Return Causes Outcome-Level Mode Collapse in Reinforcement Learning and How to Fix It with Inverse Probability Scaling
- Expected Returns and Policy Inconsistency-Aware Offline Federated Deep Reinforcement Learning
- Experience Augmented Policy Optimization for LLM Reasoning
- Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic–Procedural Memory
- Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
- Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration
- Expert-level Leaf Cell Layout Generation via Preference-Optimized LLM
- ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
- Explainable Federated Learning via Global–Local Attribution Alignment
- Explainable Forensics of Manipulated Segments in Untrimmed Long Videos
- ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
- Explaining Concept Shift with Interpretable Feature Attribution
- Explaining Data Mixing Scaling Laws
- Explanations are a Means to an End: A Value of Information Framework for Validating Explanations
- Explicitly Modeling Censoring Produces Superior Survival Predictors
- Explicit representation of germline and non-germline residues improves antibody language modeling
- Exploiting weight-space symmetries for approximating curvature
- Exploration-free Algorithms for Multi-group Mean Estimation
- Exploration Hacking: LLMs Can Learn to Resist RL Training
- Exploring 3D Dataset Pruning
- Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
- Exploring and Exploiting Stability in Latent Flow Matching
- Exploring Data-Free LoRA Transferability for Video Diffusion Models
- Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance
- Exploring Motif-based Heterogeneous Graph Learning for ReDoS Detection
- Exploring Nonlinear Pathway in Parameter Space for Machine Unlearning
- Exploring Relational Reasoning Capabilities in LLMs with REL
- Expo-GS: Exposure-Aware Signed Distance Function in Gaussian Splatting for High Dynamic Range
- Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search
- Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks
- Expressive Graph Neural Networks via Equivariant Use of Noise
- Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
- ExpWeaver: LLM Agents Learn from Experience via Latent RAG
- ExSkill: Continual Learning from Experience and Skills in Multimodal Agents
- Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
- Extending Prediction-Powered Inference through Conformal Prediction
- Extracting alignment data in open models
- Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training
- EXVERUS: Verus Proof Repair via Counterexample Reasoning
- Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
- FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over Time-Varying Directed Graphs
- FACT: Fuzzy Alignment with Comorbidity Topology for Reliable Multi-Label Medical Image Diagnosis
- FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning
- Factored Causal Representation Learning for Robust Reward Modeling in RLHF
- Factored Classifier-Free Guidance
- Factored Gossip DiLoCo: Reducing Blocking Communication within DiLoCo
- Factored Latent Action World Models
- Factored Value Functions for Graph-Based Multi-Agent Reinforcement Learning
- Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions
- Factor-Wise Homogeneity of Slot-Attention for Continual Object-Centric Learning
- Facts in Stats: Impacts of Pretraining Diversity on Language Model Generalization
- FAFO: Lossy KV Cache Compression for Lossless Inference Acceleration via Draftless Fumble Decoding
- FAIL: Flow Matching Adversarial Imitation Learning for Image Generation
- Failure-Driven Workflow Refinement
- Failure is Feedback: History-Aware Backtracking for Agentic Traversal in Multimodal Graphs
- Failure Modes in Agentic AI: Reproducible Triggers, Trace Diagnostics, and Verified Fixes
- FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
- Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off
- Fair Dataset Distillation via Cross-Group Barycenter Alignment
- Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiency
- Fair-FedMOE: Group-Fair One-Shot Federated Learning via Prototype-Guided Experts for Medical Imaging Analysis
- FairGB: A Fair Granular-Ball Generation Method for Data Classification
- FairJudge : An Adaptive, Debiased, and Consistent LLM-as-a-Judge
- FairMerging: Rethinking Model Merging through the Lens of Fairness
- Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking
- FairRARI: A Plug and Play Framework for Fairness-Aware PageRank
- FairSSL: Fair Multimodal Self-Supervised Learning
- Fair Transit Stop Placement: A Clustering Perspective and Beyond
- Faithful Mobile GUI Agents with Guided Advantage Estimator
- Faithful Relational Reasoning with Region-based Embeddings: Expressivity of Convex Coordinate-wise Models
- FakeWorld 1.0: An Omni modal Benchmark for Fake Media and Content
- Falsifying Sparse Autoencoder Reasoning Features in Language Models
- Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process
- FaPS: A General and Fast Training Method for Diffusion Models
- Fast and Accurate Causal Parallel Decoding using Jacobi Forcing
- Fast and Expressive Multi-Byte Prediction with Probabilistic Circuits
- Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
- Fast and Optimal Algorithms for Private Hypothesis Selection
- Fast and Scalable Analytical Diffusion
- Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
- Fast Byte Latent Transformer
- Faster Activation Functions at the Edge for Post-Training Speedups
- Faster Query-Key Learning Sharpens Attention in Self-Attention Models
- Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding
- FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models
- Fast Estimation for Forest Matrix of Signed Graphs
- Fast Inverse Lithography via GRPO Reinforced Flow Matching
- Fast kernel methods: Sobolev, physics-informed, and additive models
- Fast k-means Seeding Under The Manifold Hypothesis
- Fast KV Compaction via Attention Matching
- Fast Mixing Steady-State Control in Markov Decision Processes
- Fast Mixture of Curvature-Aware Experts for Diverse and Dynamic Graph Topologies
- Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding
- Fast Reconstruction of Mixtures of Bernoulli Product Distributions
- Fast-SAM3D: 3Dfy Anything in Images but Faster
- FastSESR: Fast Scene-level Explicit Surface Reconstruction
- Fast Spectrally Sparse Signal Reconstruction via Jacobi-Preconditioned Gradient Descent
- Faults in Our Formal Benchmarking: Dataset Defects and Evaluation Failures in Lean Theorem Proving
- Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints
- Feasible Fusion: Constrained Joint Estimation under Structural Non-Overlap
- Feature-aware (Hyper)graph Generation via Next-Scale Prediction
- Feature Bagging Provides Stability
- Feature Collapse Under Corruption: An Entropy Perspective on Robust Neural Networks
- Feature Resemblance: Towards a Theoretical Understanding of Analogical Reasoning in Transformers
- FedARC: Anchor-Guided Residual Compensation for Data and Model Heterogeneous Federated Learning
- FedCDWA: Decoupled Federated Prototype Distillation with Hierarchical Wasserstein Aggregation
- FEDEMOE: IMPROVING PERSONALIZATION ON HET- EROGENEOUS FEDERATED LEARNING VIA ELASTIC MIXTURE OF EXPERTS ARCHITECTURE
- Federated Bilevel Performative Prediction
- Federated Causal Inference on Multi-Site Observational Data via Propensity Score Aggregation
- Federated Data and Feature Selection by Generalized CUR Decomposition
- Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
- Federated Graph Learning via Structure-Aware Fusion Using a Kalman Framework with Learnable Dynamics
- Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
- Federated Manifold Learning (FML): Tackling Domain Heterogeneity with Structural Knowledge Transfer
- Federated Multi-view Clustering for Remote Sensing Data
- Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs
- Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
- Fedfit: Federated dynamic pruning via Fisher Information scoring
- FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning
- FedHera: Towards Drift-Resilient Federated Fine-tuning with Heterogeneous Resources
- FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
- FedPAT: Federated Test-Time Adaptation via Prototype Affinity Topology
- FedPDG: Prediction Discrepancy–Guided Data Generation for Heterogeneous Federated Learning
- FedPissa: Towards Federated Personalized Adaptation of Foundation Models via LoRA Subspace Mapping
- FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
- FedReLa: Imbalanced Federated Learning via Re-Labeling
- FedRGL: Robust Federated Graph Learning under Label Noise
- FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
- FedScar: Correcting Geometric Bias for Flatness-Consistent Federated Learning
- FedSDR: Federated Self-Distillation with Rectification
- FedSSM: State Space Model-based Proactive Inference for Heterogeneous Multimodal Federated Learning
- FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
- FedUSD: Unbiased Synthetic Data for Federated Learning
- FedVeer: Self-Adaptive Skew Estimation for Robust Federated Learning
- Feedback Control for Multi-Objective Graph Self-Supervision
- Feed-Forward Taylor-Gaussians-Flow: Towards Non-uniform Motion for Novel View Synthesis from Monocular Video
- FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning
- FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model
- FHAIM: Fully Homomorphic AIM for Private Synthetic Data Generation
- FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
- FIDIA: Function-Informed Sequence Design via Inference-Aligned Policy Optimization
- FiGuRO - Intrinsic Dimension Estimation for Multi-Modal Data
- Find, Fix, Reason: Context Repair for Video Reasoning
- Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
- Finding DoRI: Discovery of Retained Images in Diffusion Models
- Finding Most Influential Sets
- Finding Stationary Points by Comparisons
- Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
- Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
- FineFocus: Benchmarking and Improving Fine-Grained Text-to-Image Alignment via Paired Reinforcement Learning
- Fine-grained Analysis of Brain-LLM Alignment through Input Attribution
- Fine-to-Coarse Fairness-Informed Multi-View Clustering
- Fine-Tune Once, Reuse Across Models: Bayesian Task-Update Factors and Approximations
- Fine-Tuning Masked Diffusion for Provable Self-Correction
- Fine-Tuning of Transformer models with Frames
- Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
- Fingerprinting Pre-trained Encoders under Arbitrary Downstream Fine-Tuning via Adversarial Shifting
- Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
- Finite-time Convergence Analysis of Actor-Critic with Evolving Reward
- Finite-Width Neural Tangent Kernels from Feynman Diagrams
- FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting
- FIRE-Bench: Evaluating Agents on the Rediscovery of Scientific Insights
- FiRE: Fine-grained Ranking Evaluation for Machine Translation
- FIRE: Learning to Navigate and Act on Real-World Files via Stateful Reinforcement Learning
- FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models
- FiSeR: Fine-Grained Source Representations for Cross-Domain AI Image Detection
- Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control
- Fix Before Search: Benchmarking Agentic Visual Query Pre-processing in Multimodal Retrieval-augmented Generation
- Fixed Aggregation Features Can Rival GNNs
- Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors
- FiX: Introducing Fine-grained Forget Gate into Softmax Attention
- Fix the Loss, Not the Radius: Rethinking the Adversarial Perturbation of Sharpness-Aware Minimization
- Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization
- FLAC: Maximum Entropy RL via Kinetic Energy Regularized Bridge Matching
- FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction
- FLARE-AI: Flaw Reporting for AI
- FlashBlock: Attention Caching for Efficient Long-Context Block Diffusion
- Flash-GRPO: Efficient Alignment for Video Diffusion via One-Step Policy Optimization
- FlashOptim: Memory Efficient Optimizers for Large-Scale Training
- FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPU
- FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
- Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation
- FlatLab: A Unified Methodology Framework and Simulation-Based Benchmark for Robotic Manipulation of Flat Objects
- FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space
- Flatland: The Adventures of Gradient Descent with Large Step Sizes
- Flat Minima and Generalization: Insights from Stochastic Convex Optimization
- Flatness-Aware Stochastic Gradient Langevin Dynamics
- Fleet: Few-Shots Lead Effective AIGI Detection
- Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
- Flexibility-Aware Geometric Latent Diffusion for Full-Atom Peptide Design
- Flexible Kernels for Protein Property Prediction
- FlexiFlow: decomposable flow matching for generation of flexible molecular ensemble
- FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
- FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications
- FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
- Float8@2bits: Entropy Coding Enables Data-Free Model Compression
- Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients
- FloorplanQA: A Benchmark for Spatial Reasoning in LLMs using Structured Representations
- Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics
- FlowCloud: Learning Continuous Spatiotemporal Dynamics from Unpaired Sparse Point Cloud Snapshots
- Flow Equivariant World Models: Structured Memory for Dynamic Environments
- Flowers: A Warp Drive for Neural PDE Solvers
- Flow for Future: Geometric SE(3)-Equivariant Flow Matching for 3D Trajectory Prediction
- Flow Inverse Reinforcement Learning
- FlowMAP: Flow Matching for Generalizable Agent Planning
- Flow Matching Calibration for Simulation-Based Inference under Model Misspecification
- FlowNar: Scalable Streaming Narration for Long-Form Videos
- FlowPET: Physics-Informed Symplectic Flow Matching for Low-Count PET Reconstruction
- Flow Sampling : Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
- FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation
- FlowState: Sampling-Rate‑Equivariant Time‑Series Forecasting
- FluxNet: Learning Capacity-Constrained Local Transport Operators for Conservative and Bounded PDE Surrogates
- FOAM: Blocked State Folding for Memory-Efficient LLM Training
- FOAM: Frequency and Operator-Error Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo
- FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
- FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy
- Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection
- Focus and Dilution: The Multi-stage Learning Process of Attention
- FOCUS: DLLMs Know How to Tame Their Compute Bound
- FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization
- Focusing: View-Consistent Sparse Voxels for Efficient 3D VAE
- Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain
- FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance For Pruned Large Language Models
- Focus-Then-Contact: Speeding Up Robotic Contact-Rich Task Learning with Affordance-Guided Real-World Residual Reinforcement Learning
- FoeGlass: When Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors
- Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
- ForceForget: Reinforcement Concept Removal for Enhancing Safety in Text-to-Image Models
- Forecasting as a New Frontier of Intelligence
- Foreground-Aware Token Routing Vision Transformer for Real-Time Satellite Video Tracking
- ForensicConcept:Transferable Forensic Concepts for AIGI Detection
- Forensic Prompting with Dual-Action Policy Optimization for Vision-Language Forgery Detection and Localization
- Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding
- ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution
- Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
- Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
- Forgetting Whenever You Want: A Decentralized Continual Learning Framework with On-Demand Unlearning
- Forget to Know, Remember to Use: Context-Aware Unlearning for Large Language Models
- FormAct: Agentic Source Editing for Rich-Format Document Generation
- Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
- Formalizing and Falsifying Causal Pathways of Rare Events
- Formalizing Learning from Language Feedback with Provable Guarantees
- Formalizing the Binding Problem
- FormalJudge: A Neuro-Symbolic Paradigm for Agentic Oversight
- Formally Exploring Visual Anomaly Detection Evaluation Metrics
- FormalRx: Rectify and eXamine Semantic Failures in Autoformalization
- Forward-Chaining Temporal Point Process
- Forward-KL Convergence of Time-Inhomogeneous Langevin Diffusions
- Foundation Inference Models for Ordinary Differential Equations
- Foundation Models for Structured Data (FMSD @ ICML 2026)
- Foundations of Deep Generative Models: Understanding Memorization, Generalization, and Reasoning
- Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks
- Foundation VAE for CT Reconstruction, Augmentation, and Generation
- FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation
- Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
- FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
- FOVI: A biologically-inspired foveated interface for deep vision models
- Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
- FPTQuant: Function-Preserving Transforms for LLM Quantization
- FRACTAL: State Space Model with Fractional Recurrent Architecture for Computational Temporal Analysis of Long Sequences
- Fractional is Better: Learnable Derivative Orders in Neural Operator Learning
- FrameOracle: Learning What to See and How Much to See in Videos
- FreeRet: MLLMs as Training-Free Retrievers
- FreeText: Training-Free Text Rendering via Attention Localization and Spectral Glyph Injection
- Frequency-Aware Perceptual Optimization for Low-Complexity Implicit Image Compression
- Frequency Matching in Spiking Neural Networks for mmWave Sensing
- Frequentist Consistency of Prior-Data Fitted Networks for Causal Estimation
- Frictional Q-Learning
- FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
- FRISM: Fine-Grained Reasoning Injection via Subspace-Level Model Merging for Vision–Language Models
- From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion
- From Absolute to Relative: Rethinking Reward Shaping in Group-Based Reinforcement Learning
- From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model
- From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs
- From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing
- From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
- From Bits to Rounds: Parallel Decoding with Exploration for Diffusion Language Models
- From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models
- From Coarse to Fine: Deep Prototype Refinement Network for Few-Shot Point Cloud Semantic Segmentation
- From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
- From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations
- From Correspondence to Actions: Human-Like Multi-Image Spatial Reasoning in Multi-modal Large Language Models
- From Denoising to De-Channeling: Integrating Physical Channel Priors into Diffusion Models for Radio Signal Understanding
- From Diagrams to Code: Multilingual Programming with Visual Design
- From Directions to Regions: Decomposing Activations in Language Models via Local Geometry
- From Distribution to Geometry: Stable Graph Generalization via Invariant Barycenters
- From Drift to Coherence: Stabilizing Beliefs in LLMs
- From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide ML Interatomic Potential Architectures
- From Extraction to Deduction: Resolving Functional Misalignment in RAG via a Collaborative Critic-Reasoner Framework
- From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
- From Feasible to Practical: Pareto-Optimal Synthesis Planning
- From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity
- From Frames to Stories (F2S): Toward Reliable, Controllable and Trustworthy Long-Horizon Video Generation
- From Generalist to Specialist Representation
- From Generative to Episodic: Sample-Efficient Replicable RL
- From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints
- From Growing to Looping: A Unified View of Iterative Computation in LLMs
- From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion
- From Holo Pockets to Electron Density: GPT-style Drug Design with Density
- From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale
- From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
- From Individual Calibration to Reliable Classifiers: ALD Parameterization with mPAIC Guarantees
- From Inpainting to Editing: Unlocking Robust Mask-Free Visual Dubbing via Generative Bootstrapping
- From Intent to Solver Code: Semantic Alignment in Optimization Modeling
- From Interactions to Principles: Experience-Driven Self-Distillation for Evolving LLM Agents
- From Interaction Trajectories to Prompt Rules: Credit Assignment for Multi-Agent Prompt Optimization
- From Internal Diagnosis to External Auditing: A VLM-Driven Paradigm for Data-Free Online Backdoor Defense
- From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
- From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas
- From LLM-Generated Conjectures to Lean Formalizations: Automated Polynomial Inequality Proving via Sum-of-Squares Certificates
- From Lyapunov Analysis to Algorithm Design in two-sided PL Minimax Optimization
- From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
- From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning
- From Muon to Gluon: Bridging Theory and Practice of LMO-based Optimizers for LLMs
- From Noise to Control: Parameterized Diffusion Policies
- From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
- From Observations to States: Latent Time Series Forecasting
- From Optimization to Generalization under Heavy-Tailed Data: The Role of Gradient Clipping
- From Outcomes to Actions: Leveraging Hindsight for Long-Horizon Language Agent Training
- From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
- From Pairwise Affinities to Functional Correspondences: Rethinking Attention
- From Parameter Dynamics to Risk Scoring: Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
- From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment
- From Parameters to Feature Space: Task Arithmetic for Backdoor Mitigation in Model Merging
- From Patches to Plans: Reasoning Distillation for Repository-Level Program Repair
- From Perception to Planning: Evolving Ego-Centric Task-Oriented Spatiotemporal Reasoning via Curriculum Learning
- From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
- From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models
- From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory
- From Poisoned to Aware: Fostering Backdoor Self-Awareness in LLMs
- From Prior to Pro: Efficient Skill Mastering via Distribution Contractive RL Finetuning
- From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
- From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoning
- From Reasoning Traces to Reusable Modules: Reinforcement Learning for Compositional Generalization in Language Model Reasoning
- From Representation to Action: A Unified Laplacian Framework for Spatial Representation and Path Planning
- From Retrieval to Translation: Translating Query into Graph-level Clues for Retrieval-Augmented Generation
- From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
- From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
- From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning
- From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching
- From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Release for Offline-to-Online Reinforcement Learning
- From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors
- From Talking to Singing: A New Challenge for Audio-Visual Deepfake Detection
- From Teacher Pathways to Invariant Manifolds: Consensus Subspace Distillation for TSFMs
- From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space
- From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction
- From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG
- From Welfare to Utility: Generalized Objectives in Budget-Feasible Procurement
- From Winning to Understanding: A Diagnostic Long-Horizon RTS Benchmark for LLMs
- From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection
- FrontierCS: Evolving Challenges for Evolving Intelligence
- Frontier Models Can Take Actions at Low Probabilities
- Front-Loaded Robust Conformal Prediction: Heavy Calibration, Minimal Test-Time Cost
- FS-I2P: A Hierarchical Focus–Sweep Registration Network with Dynamically Allocated Depth
- FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
- Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning
- Full-Spectrum Graph Neural Network: Expressive and Scalable
- FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation
- Fully Dynamic Coreset Spectral Clustering
- Fully Zero-Shot Image Dehazing
- FunCQNet: A Functional Censored Quantile Neural Network for Predicting Long-Term Post-Transplant Kidney Survival
- Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces
- Functional building blocks of neural networks: from network motifs to collective dynamics
- Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents
- Functional Decomposition and Shapley Interactions for Interpreting Survival Models
- Functional Equivalence in Attention: A Comprehensive Study with Applications to Linear Mode Connectivity
- Function-Valued Causal Influence in Nonlinear Time Series
- FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
- Furina: Fragmented Uncertainty-Driven Refusal Instability Attack
- FUSE: Ensembling Verifiers with Zero Labeled Data
- FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation
- FUSE: Frequency-domain Unification and Spectral Energy Alignment for Multi-modal Object Re-Identification
- FuseFSS: Efficient Secure LLM Inference with Function Secret Sharing
- FUSE: Full‑spectrum Unlearnable Examples via Spectral Equalization
- FUSE: Quantifying Uncertainty in Multimodal LLMs by Bayesian Fusing Epistemic and Aleatoric Uncertainty
- FusionCell: Cross-Attentive Fusion of Layout Geometry and Netlist Topology for Standard-Cell Performance Prediction
- Future Dynamic 3D Reconstruction: A 3D World Model with Disentangled Ego-Motion
- Future-Gain Guided Test-Time Learning for Large Language Models
- FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs
- G$^2$RPO: Geometric GRPO; Escaping LLM's Reasoning Rut to Break Accuracy--Entropy Trade-off
- G$^2$TAM: Geometry Grounded Track Anything Model
- GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function
- GADA: Geometry-Aware Deformable Aggregation for Image-Based Gaussian Splatting
- GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert
- GameDevBench: Evaluating Agentic Capabilities Through Game Development
- Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery
- Game Theory in Nature: From Optimality to Equilibrium
- GameVerse: Can Vision-Language Models Learn from Video-based Reflection?
- Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking
- GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation
- GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation
- Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
- Gateways to Tractability for Satisfiability in Pearl’s Causal Hierarchy
- Gauge-Equivariant Graph Networks via Self-Interference Cancellation
- GAUSS: Graph-Assisted Uncertainty Quantification using Structure and Semantics for Long-Form Generation in LLMs
- Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
- GaussTrace: Provenance Analysis of 3D Gaussian Splatting Models with Evidence-based LLM Reasoning
- GCIB: Graph Contrastive Information Bottleneck for Multi-Behavior Recommendation
- GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
- Gecko: A Simulation Environment with Stateful Feedback for Refining Agent Tool Calls
- GemDepth: Geometry-Embedded Features for 3D-Consistent Video Depth
- GEM-FI: Gated Evidential Mixtures with Fisher Modulation
- GEM: Geometric Entropy Mixing for Optimal LLM Data Curation
- GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
- GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
- GenAlign: Towards Unified Alignment Framework of MLLMs via Generative Reward Model
- GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design
- GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
- GENEB: Why Genomic Models Are Hard to Compare
- General Analysis of LMO-based Optimizers: Beyond Bounded Variance
- General and Efficient Steering of Unconditional Diffusion Models
- General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling
- Generalist Graph Anomaly Detection via Prototype-Based Distillation
- Generalizable and Actionable Parts Pose Estimation with Symmetry Annotation-Free Learning Strategy
- Generalizable and Composable Multi-Model Embedding Translation
- Generalization and Scaling Laws for Mixture-of-ExpertsTransformers
- Generalization Bounds for Discrete Diffusion: Statistical Advantage of Masking
- Generalization Bounds for Out-of-distribution Generalization
- Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime
- Generalized Boundary FDR Control under Arbitrary Dependence: An Approach on Closure Principle
- Generalized Correctness Models: Learning Calibrated and Cross-Model Correctness Predictors from Historical Patterns
- Generalized Discrete Diffusion with Self-Correction
- Generalized Linear Bandits with Memory
- Generalized Schrödinger Bridge on Graphs
- Generalizing Multi-Scale Time-Series Modeling with a Single Operator
- Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
- General Quantification of Covariate and Concept Shifts
- General Synthetic-Powered Inference
- Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation
- Generation is Required for Data-Efficient Perception
- Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
- Generative Augmented Inference
- Generative Inverse Design with Abstention via Diagonal Flow Matching
- Generative Large Neighborhood Search: Scalable Set Cover Optimization via Discrete Diffusion
- Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
- Generative Modeling of Irregular Time Series via SDE-Induced Continuous-Discrete Variational Inference
- Generative Modeling with Probabilistic Constraints
- Generative Neural Operators through Diffusion Last Layer
- Generative Online Reinforcement Learning
- Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion
- Generative Visual Code Mobile World Models
- GenExam: A Multidisciplinary Text-to-Image Exam
- Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
- GenShield: Unified Detection and Artifact Correction for AI-Generated Images
- GenUnfold: Rapidly Predict Protein Mechanical Unfolding Trajectory via a Physics-Guided Diffusion Model
- GeoAlign: Geometric Rollout Curation for Robust LLM Reinforcement Learning
- Geodesic Calculus on Implicitly Defined Latent Manifolds
- Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
- GeoDM: Geometry-aware Distribution Matching for Dataset Distillation
- GeoEvo: Identity-Aware Potential Game with Geometric Evolution for Personalized Multimodal Federated Learning
- GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in OD Flow Prediction and Generation
- GeoLoom: High-quality Geometric Diagram Generation from Textual Input
- Geometrically Constrained Outlier Synthesis
- Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport
- Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts
- Geometric Coherence Learning for Structuring Value Functions in Plain MDPs
- Geometric Collapse: When Vision Models Fail to Verify Physical Causality
- Geometric Conformal Prediction with Spatial Ranks and Multivariate Quantiles
- Geometric Control of Out-of-Distribution Shift in Safe Offline RL
- Geometric Convergence of Gauss–Newton for Neural Networks: Riemannian Geometry and Adaptive Damping
- Geometric Decoupling: Diagnosing the Structural Instability of Latent
- Geometric Embedding Alignment via Curvature Matching in Transfer Learning
- Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory
- Geometric Flow Grounding: A Unified Manifold Decoupling Framework for Dynamics Discovery and Verification
- Geometric Pocket-Centric Protein Encoding for Polypharmacology-Guided Multi-Target Drug Design
- Geometric Rate–Distortion Invariance for Domain Generalization
- Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
- Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
- Geometry-Aware Dataset Condensation for Diffusion Model Training
- Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models
- Geometry-Aware Image Flow Matching
- Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
- Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
- Geometry-Aware Tabular Diffusion
- Geometry-based Schrödinger Bridges for Trustworthy Multimodal Fusion
- Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
- Geometry-Guided Generative Representation for Functional Brain Graphs
- Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning
- Geometry-Misalignment in Distributional Learning
- Geometry of Reason: Spectral Signatures of Valid Mathematical Reasoning
- Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in Reinforcement Learning
- Geometry-Preserving Unsupervised Alignment for Heterogeneous Foundation Models
- GeoMoLa: Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
- GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
- GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction
- GeoSense: Internalizing Geometric Necessity Perception for Multimodal Reasoning
- GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models
- GEPC: Group-Equivariant Posterior Consistency for Out-of-Distribution Detection in Diffusion Models
- GFedCL: Graph-Based Federated Continual Learning with Spatial and Temporal Awareness
- GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
- GFMate: Empowering Graph Foundation Models with Pre-training-agnostic Test-time Prompt Tuning
- GHOST: Geometry-Guided Hallucination of Opaque Surface Textures
- GHOST: Unmasking Phantom States in Mamba2 via Grouped Hidden-state Output-aware Selection & Truncation
- GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
- GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
- GI-GCN: Global Interacted Graph Convolutional Networks via Dominant Sets for Graph Classification
- GIPO: Gaussian Importance Sampling Policy Optimization
- GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry
- Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings
- GKD-Recruiter: Jointly Modeling Social and Task Heterogeneity for Spatial Crowdsourcing via Graph Knowledge Distillation
- GLAD: Bidirectional Structure-Attribute Alignment via Latent Graph Diffusion Models
- GLARE: Scalable Neuro-Symbolic Reward Shaping for LLM Agents via Group-Level Automata
- Glimpse: Geometry Learning of Multi-scale Structural Priors for 3D Pose Estimation
- Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation
- Global Credit Assignment via Dynamical Criticality
- Global Directional Priors with Local Statistical Validation for Scalable Causal Discovery
- Global Geometry Is Not Enough for Vision Representations
- Global Merger-Arbitrage Forecasting with Language Models
- Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
- Global Policy-Space Response Oracles for Two-Player Zero-Sum Games
- Goal-Conditioned Agents that Learn Everything All at Once
- Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
- GOCM: Single-Step Graph Outlier Synthesis via Origin Consistency Model
- Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text
- GoodDiffusion: Proactive Copyright Protection for Diffusion Generative Models via Learnable Sample-specific Signatures
- Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning
- GO-PRE：Goal-Oriented Next-Best-View Selection via Predictive Rendering Entropy for Active 3D Reconstruction
- GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
- GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
- gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
- GPan-LoRA: Gaussian Process Amortized Networks for Bayesian Low-Rank Adaptation in Large Language Models
- Gradient-Aware Scheduling: Coupling Curriculum and Staleness for Async Reinforcement Learning
- Gradient-Based Causal Tree Ensembles: A Backbone Architecture for Heterogeneous Treatment Effects
- Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
- Gradient Descent with Large Step Size Restores Symmetry in Deep Linear Networks with Multi-Pathway
- Gradient Flow Dynamics and Implicit Bias of Diagonal Linear Networks under Infinitesimal Initialization
- Gradient Flow Sampler-based Distributionally Robust Optimization
- Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions
- Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
- Gradient Inversion Attacks Beyond SGD
- Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
- Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization
- GradientStabilizer: Fix the Norm, Not the Gradient
- Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Model
- Gradient Transformer: Learning to Generate Updates for LLMs
- GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent
- GradPower: Powering Gradients for Faster Language Model Pre-Training
- Gram2Token: Enabling Run-time GPU-Native Grammar-Constrained Decoding for LLMs
- G-RANS: Generalizable Residual-Aware Neural Solvers for Sparse Systems
- Granularity-Aware Adaptive Classifier Expansion via Zero-Shot Learning
- GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval
- Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings
- Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
- GraphFLEx: Unsupervised Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s
- GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
- Graph Foundation Models: A New Era for Graph Machine Learning
- Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
- Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
- Graph is a Substrate Across Data Modalities
- Graph-Link: Bridging the Semantic-Structural Gap in Text-to-SQL via Constrained Subgraph Induction
- Graph Neural Dynamics via Learned Energy and Tangential Flows
- Graph Neural Networks Are Not Continuous Across Graph Resolutions
- Graph of States: Solving Abductive Tasks with Large Language Models
- GraphP-FL: Personalized Federated Graph Learning via Dynamic Structure Awareness and Fisher Information Elastic Alignment
- GraphPFN: A Prior-Data Fitted Network for Graph Node-Level Tasks
- Graph-Preference Learning: Debiasing Network-Sampled Human Feedback for Target Welfare Estimation
- Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
- Graph Rewiring based on Flow Alignment for Improving Fluid Simulation
- GRASP: Awakening Latent Spatial Reasoning in LVLMs via Training-free Geometric Rectification
- GRASP: Graph Reasoning via Agentic Solving and Probing of LLMs
- Great Minds Think Alike: Contextual Tacit Communication for Decentralized LLM-Agent Cooperation
- Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance
- GR-LoRA: Gradient-Recycling Low-Rank Adaptation for Class-Incremental Learning
- Grokking Finite-Dimensional Algebra
- Gromov-Wasserstein at Scale, Beyond Squared Norms
- Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logs
- Grounding Functional Similarity by Invariance-Aware Model Stitching
- Grounding LLMs in Scientific Discovery via Embodied Actions
- Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
- Group Cognition Learning: Making Everything Better Through Controlled Two-Stage Agents Collaboration
- Group Distributionally Robust Optimization-Driven RL for LLM Reasoning
- Group-wise Data Ordering: Enhancing Instruction Tuning of Large Language Models via Embedding Proximity
- Grouter: Decoupling Routing from Representation for Accelerated MoE Training
- GRPO-based Cluster Decision Agent for Unknown-$\boldsymbol{K}$ Multi-view Clustering
- GRPO is Secretly a Process Reward Model
- GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors
- GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache
- GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy
- Guaranteed Optimal Compositional Explanations for Neurons
- GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
- Guidance: Sentence-Level Citation Enforcement via Prefix-Tail Guidance during LLM Decoding
- GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance
- Guided Star-Shaped Masked Diffusion
- Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
- GUI-Spotlight: Adaptive Iterative Focus Refinement for Enhanced GUI Visual Grounding
- GXPO: Group Cross-Lingual Relative Policy Optimization for Code Generation
- H$^2$CL: Heterogeneity-Aware Hypergraph Contrastive Learning for Robust Representation
- h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
- Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment
- Hallucination Detection from Structural Reasoning Model
- Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing
- HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning
- HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
- Hamiltonian Asymmetric Fusion: One-Way Safe Directed Refinement under Modality Imbalance
- Hard-Constrained Graph Generation with Discrete-Projection Diffusion
- HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression
- Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift
- Hardware-Aware Dynamic Sparse Training for Large Output Spaces
- Harmful Overfitting in Sobolev Spaces
- Harmonized Dual Policy Improvement for Model-based Reinforcement Learning
- Harnessing Non-Adversarial Robustness in Large Language Models
- Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping
- Harnessing Spectrum Video for Subject-Level Few-Shot and Cross-Montage EEG Generalization
- Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents
- HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
- HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
- Head-in-Head in Linear Attention
- Hearing Without Noticing? Attention-Aware Stealthy Black-box Adversarial Audio Attacks
- HEARTS: Benchmarking LLM Reasoning on Health Time Series
- Heavy-tailed Physics-Informed Neural Networks
- HECTOR: Hybrid Editable Compositional Object References for Video Generation
- Hedging on the frontier: Learning new tasks with few samples
- HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
- HelioX: A GPU-Native Framework for Simulation and Training of Biophysically Detailed Networks
- HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation
- Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
- HeraSys: Collaborative Serving of Multiple LLM Workflows via Fine-Grained End-to-End Optimization
- Hermes: An Evidence-Driven Agentic Framework for Trustworthy and Explainable AI-Generated Video Detection
- HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs
- Hermite-NGP: Gradient-Augmented Hash Encoding for Learning PDEs
- HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench
- Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning
- Heterogeneous Customizable Personalized Federated Fine-Tuning Approach for Large Language Models
- HexGen-3: A Fully Disaggregated LLM Serving Framework with Fine-Grained Heterogeneous Resource Autoscaling
- HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction
- HGMem: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling
- HIAL: Towards Semantics-Aware Hypergraph Active Learning via Dual-Perspective Information Maximization
- HiCI: Hierarchical Construction–Integration for Long-Context Attention
- Hidden in Plain Sight -- Class Competition Focuses Attribution Maps
- Hidden in Plain Tokens: Simply Robust, Gradient-Free Watermark for Synthetic Audio
- Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models
- HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling
- Hide&Seek: Learning to explain in an end-to-end differentiable network
- HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems
- Hierarchical Abstract Tree for Cross-Document Retrieval Augmented Generation
- Hierarchical Anchor Graph Learning for Multi-View Clustering
- Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
- Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization
- Hierarchical Goal Abstractions via Learned Subset Relations
- Hierarchical Image Tokenization for Multi-Scale Image Super Resolution
- Hierarchical Multi Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
- Hierarchical ODE: Learning Continuous-Time Physical Prototypes for Early Link Failure Detection
- Hierarchical Policy Learning via Spectral Decomposition
- Hierarchical Procedural Meta-Reasoning for Generalizable Multimodal Agents
- Hierarchical Reinforcement Learning for Sparse-Reward Search in Commutative Algebra
- Hierarchical Representations for Cross-task Automated Heuristic Design using LLMs
- Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
- Hierarchical Successor Representation for Robust Transfer
- HieraScaffold: Learning Compact Hierarchical Representations for Scalable 4D LiDAR Generation
- HieRD: Hierarchical Relational Distillation for Vision-Language Embedding Models
- HIER: Human-in-the-Loop Imagination–Execution Refinement for General Real-World Vision-Language-Action Models
- High-accuracy and dimension-free sampling with diffusions
- High-accuracy sampling for diffusion models and log-concave distributions
- High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator
- High Dimensional Learning Dynamics: the Science of Scaling
- High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders
- Higher-Order Certified Robustness for Regression
- High-Fidelity ANN-to-SNN Conversion via Closed-Loop CKA Distillation
- High-Probability Convergence Guarantees of Decentralized SGD
- HilbertA: Hilbert-Curve–Aligned Sparse Attention for 2D Structured Data
- HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel
- HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control
- HInT: Hypergraph Infusion at the Structural Layers Improves Table Understanding
- HiPER: Hierarchical Plan–Execute RL for Multi-Turn LLM Agents
- HiPhO: How Far Are (M)LLMs from Humans in the Latest High School Physics Olympiad Benchmark?
- HiPPO Zoo: Making Implicit State Space Memory Explicit
- Hista and Numca: Estimate State Value Effectively for Large Language Model Reinforcement Learning
- HiST: A Hierarchical Sparse Transformer for Cross-Modal Spatial Transcriptomics Modeling
- History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction
- HIVE-3D: Hierarchical Voxel Enhancement for High-Quality 3D Scene Generation
- HOBIT: Hardness Optimized Batch Sampling for InfoNCE Training
- HodgeFlow Policy Search by Topologically Dissecting Temporal-Difference Signals in Non-Markovian Environments
- HOI-PAGE: Zero-Shot Human-Object Interaction Generation with Part Affordance Guidance
- Hölder++: Improving Quality-Coherence Trade-off in Multimodal VAEs
- Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence
- HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing
- Holonomy Grid Codes for Generalisation Under Directed Actions
- Homophily-Heterogeneity Gradient Surgery for Federated Graph Learning
- Hom-PGD+: Fast Reparameterized Optimization over Non-convex Ball-Homeomorphic Set
- HONet: Data-Efficient Learning for Exact Cover Problems via Hypergraph Optimization
- HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
- How2Everything: Mining the Web for How-to Procedures to Evaluate and Improve LLMs
- How can embedding models bind concepts?
- How Can I Publish My LLM Benchmark Without Giving the True Answers Away?
- How Can Mamba Learn In Context with Outliers and Generalize Provably?
- How can we assess human-agent interactions? Case studies in software agent design
- How Chain of Thought Decomposes Complex Tasks
- How does Bayesian Sampling help Membership Inference Attacks?
- How does information access affect LLM monitors' ability to detect sabotage?
- How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs
- How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
- How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
- How do Human Processes AI-generated Hallucination Contents: a Neuroimaging Study
- How Do Language Models Speak Languages? A Case Study on Unintended Code-Switching
- How do LLMs Compute Verbal Confidence?
- How Far Can LLM Agents Reason with Tables? Benchmarking Multi-Turn Agentic Table Question Answering in the Wild
- How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning
- How Good is Post-Hoc Watermarking With Language Model Rephrasing?
- How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
- How high is ‘high’? Rethinking the roles of dimensionality in topological data analysis and manifold learning
- How Language Models Process Negation
- How much can language models memorize?
- How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting
- How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability
- How Powerful are LLMs in Generating Program Specifications?
- How Reasoning Evolves from Post-Training Data: An Empirical Study Using Chess
- How RLHF Amplifies Sycophancy
- How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning
- How Should Transformers Represent Numeric Values in Electronic Health Records?
- How to Avoid Debate: Scalable AI Safety via Doubly-Efficient Interactive Proofs
- How to Correctly Report LLM-as-a-Judge Evaluations
- How to Fine-Tune a Reasoning Model? A Teacher–Student Cooperation Framework to Synthesize Student-Consistent SFT Data
- How to guide your flow: Steering flow maps for rapid test-time alignment
- How to Price Data: A Market Equilibrium Based Approach
- How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models
- How Transformers Represent Hierarchies: A Local-to-Global Mechanism
- HPS: Hyperspherical Parameter Sharing for Efficient Multi-Agent Reinforcement Learning
- HSGG: Training-Free Hierarchical Scene Graph Generation with Geometry-Guided Relation Reasoning
- HSMAD: Heterophily-Driven Spectral and Manifold Learning for Graph Anomaly Detection
- HTAC: Hierarchical Task-Aware Composition for Continual Offline Reinforcement Learning
- Hugging Carbon: Quantifying the Training Carbon Emissions of AI Models at Scale
- HugRAG: Hierarchical Causal Knowledge Graph Design for RAG
- Human-AI Collaborative Uncertainty Quantification
- Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning
- HumanLM: Simulating Users with State Alignment Beats Response Imitation
- Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models
- HVAE: Hyperbolic Variational Autoencoder For Flexible Knowledge Transfer Across Multiple Domains
- HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis
- HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference
- Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
- HybridOM: Hybrid Physics-Based and Data-Driven Global Ocean Modeling with Efficient Regional Downscaling
- Hybrid Policy Distillation for LLMs
- Hybrid Reinforcement Learning in Adversarial Markov Decision Processes
- Hydra-Nav: Object Navigation via Adaptive Dual-Process Reasoning
- HyMTRL: A Hybrid Multi-Task Reinforcement Learning Framework via Phased Policy Evolution
- HypCL: Adapting CLIP in Hyperbolic Space for Continual Learning
- Hyperbolic Associative Memory Networks
- Hyperbolic Hierarchical Alignment for Video-Based Visible-Infrared Person Re-Identification
- Hyperbolic Multimodal Continual Learning
- Hyperbolic Neural Operator
- Hyperbolic neural population geometry benefits computation
- Hyperbolic RQ-VAE enhanced Generative Recommendation with Differential-Length Codebook Strategy
- HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction
- Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning
- Hyper-LLaVA: Hyperbolic Uncertainty-aware Modality-Balanced Routing for Multimodal Continual Instruction Tuning
- HyperMLP: An Integrated Perspective for Sequence Modeling
- Hyperparameter Transfer Laws for Non-Recurrent Multi-Path Neural Networks
- Hyperparameter Transfer with Mixture-of-Expert Layers
- HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection
- Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism
- HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
- HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds
- HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation
- IACW: Intent-Aware Controllable Watermarking for Scalable Authorial Intent Attribution
- IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning
- IBMA: Information Bottleneck-Based Multimodal Alignment
- ICML 2026 Hypothesis Testing Workshop
- ICR-RL: Deep Reinforcement Learning via In-Context-Regression
- Ideal Attribution and Faithful Watermarks for Language Models
- Identifiable Equivariant Networks are Layerwise Equivariant
- Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
- Identifiable Nonlinear Differentiable Causal Discovery via Independence and Adaptive Group Sparsity
- Identifiable Smooth Conjugacy Learning via Adversarial Orthogonality
- Identifiable Token Correspondence for World Models
- Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
- Identifying and Mitigating Errors in Gradient Aggregation of Distributed Data Parallel Training
- Identifying Common Hubs in Multiple Gaussian Graphical Models
- Identifying Connectivity Distributions from Neural Dynamics Using Flows
- Identifying dependent components from multi-domain linear mixtures
- Identifying Latent Concepts and Structures for Generalized Category Discovery
- Identifying Learnwares via Reduced Neural Conditional Mean Embedding
- Identifying Partially Observed Causal Models from Heterogeneous/Nonstationary Data
- IdEst: Assessing Self-Supervised Learning Representations via Intrinsic Dimension
- IDLM: Inverse-distilled Diffusion Language Models
- IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research
- IEC: When Information-Driven Exploration Meets Spectral Consensus via Primal–Dual Reward Regularization in Decentralized Multi-Agent RL
- iGRPO: Fast Online RL for Flow Matching Model with Dense Reward
- iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
- Image Restoration via Diffusion Models with Dynamic Resolution
- Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models
- Imagination Helps Visual Reasoning, But Not Yet in Latent Space
- ImgCoT: Compressing Long Chain of Thought into Compact Visual Tokens for Efficient Reasoning of Large Language Model
- Imitation Learning for Multi-turn LM Agents via On-policy Expert Corrections
- ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning
- Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness
- IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
- Impact of Connectivity on Laplacian Representations in Reinforcement Learning
- Implicit Action Chunking for Smooth Continuous Control
- Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR
- Implicit Intelligence - Evaluating Agents on What Users Don’t Say
- Implicit Preference Alignment for Human Image Animation
- Implicit Safety Alignment from Crowd Preferences
- Implicit Turn-Wise Policy Optimization for Proactive User-LLM Interaction
- Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
- ImpQuant: Fine-Grained Importance-Aware Quantization for Large Vision-Language Models
- Improved Algorithms for Nash Welfare in Linear Bandits
- Improved Analysis of the Accelerated Noisy Power Method with Applications to Decentralized PCA
- Improved Bounds for Private and Robust Alignment
- Improved Bounds for Reward-Agnostic and Reward-Free Exploration
- Improved Convergence Analysis of Topology Dependence in Decentralized SGD
- Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variations
- Improved Distribution Estimation in $\ell_\infty$
- Improved Dynamic Algorithm for Non-monotone Submodular Maximization under Cardinality Constraint
- Improved Scaling Laws via Weak-to-Strong Generalization in Random Features Ridge Regression
- Improved Stochastic Optimization of LogSumExp
- Improving Adversarial Robustness of Attribution via Implicit Regularization
- Improving Backward Conformal Prediction via Non-Conformity Score Transformation
- Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
- Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning
- Improving Diffusion Planners by Self-Supervised Action Gating with Energies
- Improving Explicit Dynamic Gaussian Splatting Optimization via Update Mixture
- Improving Few-Shot Design Optimization By Exploiting Auxiliary Information
- Improving Graph Transformers via Global Structural Priors
- Improving LLM-Based Recommenders with Conservative Generative Flow Networks
- Improving ML attacks on LWE with data repetition and stepwise regression
- Improving Neural Topic Modeling with Semantically-Grounded Soft Label Distributions
- Improving Sampling for Masked Diffusion Models via Information Gain
- Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
- Improving the Robustness-Utility Trade-off in Decentralized Learning over Sparse Networks
- Improving the Sensitivity of Backdoor Detectors via Class Subspace Orthogonalization
- Improving Video Sparse Attention with Fine-grained Router and Sparse Rebasing
- Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference
- Improving Zero-Shot Offline RL via Behavioral Task Sampling
- ImpText: A Benchmark and Tool-Augmented Framework for Implicit Text Reasoning
- Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System
- Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
- Incomplete Multi-View Clustering via Neighborhood-Conditioned Diffusion
- Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data
- In-Context Generation with Regional Constraints for Instructional Video Editing
- In-Context Learning as Rate–Distortion Optimization
- In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
- Incorporating Importance Weighting in Optimal Transport Based Domain Alignment
- Incremental BPE Tokenization
- Incremental Learning of Sparse Attention Patterns in Transformers
- Incremental Transformer Neural Processes
- Independent Component Discovery in Temporal Count Data
- INDEXGUARD: Index-only Backdoor Vetting for Secure Federated PEFT of Large Language Models
- IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference
- Inducing LLM Workflows with Bilevel Optimization and Textual Gradients
- Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Reasoning Models
- INDUCTION: Finite-Structure Concept Synthesis in First-Order Logic
- Induction Heads Interpolate N-Grams
- Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models
- InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
- Inference-Aware Meta-Alignment of LLMs via Non-Linear GRPO
- Inference of Online Newton Methods with Nesterov's Accelerated Sketching
- Inference-time Alignment with Rewards in Besov Spaces: Provable Advantages of Feature Learning and Multi-Step Policy Updates
- Inference Time Concept Removal Guidance for Text-to-Image Diffusion Models
- Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models
- Inference-Time Forward-Process Alignment in Diffusion Models
- Inference-time optimization for experiment-grounded protein ensemble generation
- Inference Time Optimization with Confidence Dynamics
- INFER: Learning Implicit Neural Frequency Response Fields for Confined Acoustic Environments
- InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training
- Infinite-dimensional generative diffusions via Doob's h-transform
- Infinite Mask Diffusion for Few-Step Distillation
- Infinite-Precision Autoregressive Modeling for Vector Graphics and Layouts
- Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
- Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
- Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
- InfoDLM: an Information-Adaptive Framework for Discrete Diffusion Language Model Pretraining
- InfoFlow KV: Information-Flow-Aware KV Recomputation for Long Context
- InfoGeo: Information-Theoretic Object-Centric Learning for Cross-View Generalizable UAV Geo-Localization
- InfoGlobe: Local-and-Global Information-Preserving Statistical Manifold Learning for Single-Cell Transcriptomics
- InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition
- InfoPO: Information-Driven Policy Optimization for User-Centric Agents
- Information dynamics and Memory in Neural Networks through Fisher Information Diffusion
- Information Flow Reveals When to Trust Language Models
- Information-Geometric Adaptive Sampling for Graph Diffusion
- Information Geometry Loss for Time Series Forecasting
- Information-Theoretic Disentangled Latent Modeling with Conditional Diffusion for Incomplete Multi-View Clustering
- Information-Theoretic Generalization Bounds for VAEs: A Role of Encoder and Latent Variable
- Informed Asymmetric Actor-Critic: Leveraging Privileged Signals Beyond Full-State Access
- InfraRL: A Benchmark for Constrained Resource Allocation in Large-Scale Infrastructure Asset Management
- InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning
- InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
- Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior
- Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression
- Inner-layer Token Self-Modulation as Another Scaling Axis for LLMs
- InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem
- Innovation: An Almost Characterization of Hallucination
- Insertion Based Sequence Generation with Learnable Order Dynamics
- Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
- Instance-Level Costs for Nuanced Classifier Evaluation
- Instance-Specific Approximation Ratios for Correlation Clustering and Max-Cut
- InstEmb: Instruction-Following Embeddings through Glimpses of the Future
- Instruction Decomposition and Action Alignment for Vision-Language Navigation
- Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models
- Intentional Updates for Streaming Reinforcement Learning
- IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning
- InteractBench: Benchmarking LLMs on Competitive Programming under Unrevealed Information
- InteractComp: Evaluating Search Agents With Ambiguous Queries
- Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
- Interactive Person Retrieval via Multi-Turn Multimodal Conversation
- Interactive Segmentation with Elaborate Focus Prior
- InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation
- Interleaved Selective State Space Models for Efficient WiFi-Based 3D Multi-Person Pose Estimation
- Internalizing Safety Understanding in Large Reasoning Models via Verification
- Interpretability and Generalization Bounds for Learning Spatial Physics
- Interpretability Transfer from Language to Vision via Sparse Autoencoders
- Interpretable Discovery of One-parameter Subgroups: A Modular Framework for Elliptical, Hyperbolic, and Parabolic Symmetries
- Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
- Interpretable Functional Koopman Learning with Non-Markovian Closure for Spatiotemporal Systems
- Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations
- Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation
- Interpreting and Enhancing Emotional Circuits in Large Vision-Language Models via Cross-Modal Information Flow
- Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks
- Interpreting Genomic Language Models using Sparse Autoencoders
- Interpreting Physics in Video World Models
- Intervene When It Doubts: Conjunction-Guided Interactive Reasoning
- Interventional Processes For Causal Uncertainty Quantification
- In-Training Defenses Against Emergent Misalignment in Language Models
- Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
- Intrinsic Credit Assignment for Long Horizon Interaction
- Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision–Language Models
- Intrinsic Task Symmetry Drives Generalization in Algorithmic Tasks
- Introspection Adapters: Training LLMs to Report Their Learned Behaviors
- INT vs. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
- Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
- Inverse Depth Scaling From Most Layers Being Similar
- Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
- Inverting Data Transformations via Diffusion Sampling
- Investigating Advanced Reasoning of Large Language Models via Black-Box Interaction
- Investigating Component Contributions in Multi-Agent ML Systems
- Investigating Memory in RL with POPGym Arcade
- InvGNN: Learning Invertible Node Representations on Graphs
- IO-Adam: Rethinking Memory-Efficient Adaptive Optimizers from Gradient Computation
- IPMark: A Sentence-Level Watermark for LLMs with Hierarchical Personalization and Efficient Detection
- IQA-Spider: Unifying Reasoning, Grounding, and Referring for Multi-Granularity Image Quality Assessment
- IRIS: Implicit Reward-Guided Internal Sifting for Mitigating Multimodal Hallucination
- IRPM: Intergroup Relative Preference Modeling for Pointwise Generative Reward Models
- Is Code Better Than Language for Algorithmic Reasoning?
- Is Data Shapley Not Better than Random in Data Selection? Ask NASH
- Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
- Is Graph Mixup Beneficial? Investigating Interpolation And Empirical Performance of Graph Mixup Methods
- IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL
- Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
- Is Spurious Correlation Removal Always Learnable?
- Is the Last Layer Sufficient for Uncertainty Quantification?
- Is Training Necessary for Anomaly Detection?
- Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks
- Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models
- Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives
- Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation
- Iterated Population Based Training with Task-Agnostic Restarts
- Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation
- Iterative Robust Satisficing: Minimizing Performance Degradation Under Distribution Shift
- iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance
- It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents
- ITSPACE: Monotone Gaussian Optimal Transport Updates
- It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
- iVGR: Internalizing Visually Grounded Reasoning for MLLMs with Reinforcement Learning
- IVQA-LD: Inclusive Multimodal Understanding for Population with Limb-Deficiency
- IVQ: Structured and Lightweight Vector Quantization via Binary Hierarchical Composition Inspired by $\textit{IChing}$
- iWorld-Bench: A Benchmark for Interactive World Models with a Unified Action Generation Framework
- JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
- JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG
- JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks
- JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments
- Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking
- Jailbreaking Vision-Language Models Through the Visual Modality
- Jailbreak to Protect: Buffering Harmful Fine-Tuning via Temporary Jailbreaking LoRA in Large Language Models
- JANUS-LORA: A Balanced Low-Rank Adaptation for Continual Learning
- JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
- Joint-Embedding Predictive Learning of Latent Market States in U.S. Equities
- Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
- Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution
- Joint Learning in the Gaussian Single Index Model
- Joint Model and Data Sparsification via the Marginal Likelihood
- Joint Navigation and Manipulation Planning with 3D Interaction Chains
- Joint-Space Empowerment as a Theory of Dexterous Motor Coordination
- Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math
- Judgment Operators: A Composition-Invariant Substrate for Multi-Agent Action Spaces
- Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs
- Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
- Just Noticeable Difference Modeling for Deep Visual Features
- Just Y-Prediction: Enabling Historical Cumulative Inconsistency in Label Diffusion for Learning with Noisy Label
- KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning
- Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modeling and State Tracking
- KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
- KAST-BAR: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for Universal Neural Interpretation
- KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering
- Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies
- Keeping a Secret Requires a Good Memory: Space Lower-Bounds for Private Algorithms
- Keep It in Mind: User Centric Continual Spatial Intelligence Reasoning in Egocentric Video Streams
- KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Bandits
- Kernel-based Maximum-of-difference Test for Two-sample Comparison
- KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware
- KernelFoundry: Hardware-Aware Evolutionary GPU Kernel Optimization
- KFStego: Key-Free Secure Image Distribution via Bipartite Structural Invariants
- KineFlow: Kinematic Second-Order Flow Matching for Time-Series Forecasting
- Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes
- KITE: Knowledge-Guided Probabilistic Modeling for Time Series Forecasting with Exogenous Variables
- Klein Hyperbolic Metric Learning
- Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation
- KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem
- Knothe-Rosenblatt Quantile Regression for Risk-sensitive Multi-objective Reinforcement Learning
- Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement
- Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
- Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning
- Knowing Who, Not How Much: Learning-Augmented Mechanisms for Consumer Utility Maximization
- Knowledge Diversion for Efficient Morphology Control and Policy Transfer
- Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
- Know Thyself, Know Thy User: Intrinsic Dual-Perspective Reasoning for Role-Playing LLMs
- KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls
- Krause Synchronization Transformers
- KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices
- Kronecker Generative Networks: A General Neural Architecture for Parameter-Efficient Learning Across Classification Tasks
- KUMA: A Novel Framework with Koopman Separation and Efficient Multilevel Extraction in Time Series Forecasting
- Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
- L2G-NET: Local to Global Spectral Graph Neural Networks via Cauchy Factorizations
- LabBuilder: Protocol-Grounded 3D Layout Generation for Interactable and Safe Laboratory
- Label-Guided Representation Learning for Incomplete Multi-View Multi-Label Classification
- Lab-in-the-Loop for Drug R&D with AI
- LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
- LAGEA: Language Guided Embodied Agents for Robotic Manipulation
- LagLLM: LLM-empowered lead–lag dependency learning for spatial-temporal time series forecasting
- Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
- LakeQA: A Benchmark for Complex Exploratory QA over a Million-Scale Data Lake
- LALM-as-a-Judge: Benchmarking Large Audio-Language Models for Safety Evaluation in Multi-Turn Spoken Dialogues
- LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
- Landmark-Guided Policy Optimization for Multi-Objective Language Model Selection
- Langevin Rollout Optimization for Modelic Reinforcement Learning
- LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries
- LangPrecip: Language-Aware Multimodal Precipitation Nowcasting
- Language as a Wave Phenomenon: Semantic Phase Locking and Interference in Neural Networks
- Language-based Trial and Error Falls Behind in the Era of Experience
- Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation
- Language Generation in the Limit: Complexity Barriers and Implications for Learning
- Language Generation with Feedback: Queries and Mistakes
- Language Generation with Replay: A Learning-Theoretic View of Model Collapse
- Language Model Augmented Semi-Supervised Statistical Inference
- Language Model Circuits Are Sparse in the Neuron Basis
- Language Models as Nodes: Constructing a High-Level Neural Network
- Laplacian Representations for Decision-Time Planning
- LAPRAS : Learning-Augmented PRivate Answering for linear query Streams.
- LaRA-Fusion: Latent-Robust Adaptation via Dual-Loop Constraints for Infrared and Visible Image Fusion
- LARA: Latent Action Representation Alignment for Vision-Language-Action Models
- LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
- Large-capacity and Receiver Authenticable Generative Image Steganography
- Large Language Model Agents Are Not Always Faithful Self-Evolvers
- Large Language Models as Topological Thinkers: A Benchmark on Graph Persistent Homology
- Large Language Models Develop Novel Social Biases Through Adaptive Exploration
- Large Language Models Explore by Latent Distilling
- Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge
- Large Scale Manifold Balanced Clustering
- Large-Scale Molecular Dynamics Simulations: Direct Interatomic Modeling with Dilated Message Passing
- Large-Scale Notification Dispatch with Bundle Treatments and Multi-Outcome Uplift Optimization
- Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments
- Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
- Large Vision–Language Models Get Lost in Attention
- LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning
- LASER: Learning Active Sensing for Continuum Field Reconstruction
- LassoFlexNet: a Flexible Neural Architecture for Tabular Data
- LaST$_{0}$: Latent Spatio-Temporal Chain-of-Thought for Robotic Vision-Language-Action Model
- LAST: Bridging Vision-Language and Action Manifolds via Gromov-Wasserstein Alignment
- Last-iterate Convergence of ADMM on Multi-affine Quadratic Equality Constrained Problem
- Last-Iterate Convergence of Regularized Gradient Methods for Stochastic Monotone Variational Inequalities
- LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
- Latent Collaboration in Multi-Agent Systems
- Latent Diffusion Controller: Framework, Algorithms and Parameterization
- Latent Diffusion Pretraining for Crystal Property Prediction
- Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation
- Latent-Guided Cooperative Energy-Based Models
- Latent Guided Sampling for Combinatorial Optimization
- Latent Laplace Diffusion for Irregular Multivariate Time Series
- LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs
- Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models
- Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
- Latent Space Robust Optimization of Neural Processes with Aligned Stratified Order-Statistic Loss Reduction
- Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
- Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens
- LATMiX: Learnable Affine Transformations for Microscaling Quantization of LLMs
- LATO: 3D Mesh Flow Matching with Structured TOpology Preserving LAtents
- LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer
- LAVA: A Unified Framework for Finetuning Language and Vision Models
- Lavida-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models
- Layer-Centric Factors of Variation Disentanglement for Task- and Model-Agnostic Generalization
- LayerT2V: A Unified Multi-Layer Video Generation Framework
- Layer-wise Gradient Disentanglement: Decoupling Semantics and Preferences in Direct Preference Optimization
- LazyAttention: Efficient Retrieval-Augmented Generation with Deferred Positional Encoding
- LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
- L-CUBE: Isolating Long-Context Capacity from Knowledge with Controllable Mutual Information Scaling
- LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
- L-Drive: Beyond a Single Mapping—Latent Context Drives Time Series Forecasting
- Leaderboard Incentives: Model Rankings under Strategic Post-Training
- Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding
- LeakGFN: Robust Molecular Generation in Generative Flow Networks via Flow Decomposition
- LEAP: Zone-Aware MCTS for LLM Self-Speculative Decoding
- Learnability-Driven Knowledge Assimilation for Class-Incremental Semantic Segmentation
- Learnability-Informed Fine-Tuning of Diffusion Language Models
- Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
- Learn from A Rationalist: Distilling Intermediate Interpretable Rationales
- Learn from Your Mistakes: Tree-like Self-Play on Vulnerability Nodes for Secure Code LLMs
- LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration
- Learning $U$-Statistics with Active Inference
- Learning 3D-Gaussian Simulators from RGB Videos
- Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction
- Learning Adaptive Topology with FiLM-Guided Distillation for Tertiary Structure-Based RNA Design
- Learning a Generative Meta-Model of LLM Activations
- Learning Anisotropic Value Geometry with Finsler Reinforcement Learning
- Learning Attribute–Affordance Hierarchies in Hyperbolic Space for Open-Vocabulary 3D Object Affordance Grounding
- Learning-Augmented Online Covering Problems
- Learning-Augmented Online Minimization with Dual Predictions
- Learning-augmented Rent-or-Buy with a Sample
- Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts
- Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs
- Learning Biophysical Models of Large-Scale Multineuronal Data To Enable Precise Neurostimulation
- Learning Cardiac Latent Representations in Vectorcardiogram Space
- Learning Coherent Representations: A Topological Approach to Interpretability
- Learning Compressed Shape-Aware Molecular Representations for Virtual Screening
- Learning Context-Conditioned Predicate Semantics via Prototype Feedback
- Learning Coupled Continuous-Time Latent Dynamics from Irregular Events
- Learning Credal Ensembles via Distributionally Robust Optimization
- Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic
- Learning Discrete Diffusion on Graphs via Free-Energy Gradient Flows
- Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
- Learning Disentangled Multi-Agent World Model for Decentralized Control
- Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective
- Learning Efficient Guardrails for Compliance
- Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
- Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions
- Learning from Comparison: Constrained Projection Policy Optimization for Pareto-Front Improvement
- Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
- Learning from Pairwise Preferences in Long-Term Decision Problems
- Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
- Learning Gaussian Mixture-distributed Prototypes for 3D Scene Graph Generation from RGB-D Sequences
- Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
- Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL
- Learning Generalized Label Distributions
- Learning Generalized Trackers with Elastic Token Budgets
- Learning Global Representation from Queries for Vectorized HD Map Construction
- Learning Graph Foundation Models on Riemannian Graph-of-Graphs
- Learning-Guided Integration Contours Construction for Fast Large-Scale Generalized Eigensolvers
- Learning GUI Grounding with Spatial Reasoning from Visual Feedback
- Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
- Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
- Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
- Learning High-Frequency Continuous Action Chunks in Latent Space
- Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization
- Learning in Bayesian Stackelberg Games With Unknown Follower's Types
- Learning in Structured Stackelberg Games
- Learning Interpretable Options by Identifying Reward Diffusion Bottlenecks in Reinforcement Learning
- Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning
- Learning Junta Distributions, Quantum Junta States, and QAC$^0$ Circuits
- Learning Latent Action World Models In The Wild
- Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
- Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
- Learning Manifold and Itô Dynamics with Branched Neural Rough Differential Equations
- Learning Manifold Data with Flow Matching
- Learning Molecular Semantic Invariant Representation with Prototype Constraint
- Learning More from Less: Unlocking Internal Representations for Benchmark Compression
- Learning Multi-Agent Coordination via Sheaf-ADMM
- Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis
- Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
- Learning on Higher-Order Structures with Effective Operators
- Learning Partial Concept Classes and Universal Rates Under Massart Noise
- Learning Permutation Distributions via Reflected Diffusion on Ranks
- Learning Permutation from Structure Without Supervision
- Learning Permutation-invariant Macroscopic Dynamics
- Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
- Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
- Learning Randomized Reductions
- Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
- Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning
- Learning Realistic Depth via Physics-Grounded Noise Disentanglement with Semantic-Geometric Collaboration
- Learning Reward–Cost Balance in Safe RL via Score-Based World Models
- Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
- Learning Rewrite-Invariant Reasoning with Targeted Alternation Training
- Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs
- Learning Situated Awareness in the Real World
- Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
- Learning Sparse Visual Representations via Spatial-Semantic Factorization
- Learning Stochastic Bridges for Video Object Removal via Video-to-Video Translation
- Learning Structured Reasoning via Tractable Trajectory Control
- Learning syntax without semantics: Disentangled tiny language models
- Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
- Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models
- Learning the Best Under Constraints: A Duality-Based Framework
- Learning the ESG Geometry with Domain Aware Language Models
- Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
- Learning the Minimum Action Distance
- Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
- Learning Tight Rejection Boundaries without Negatives for Strict One-Class Audio Deepfake Detection
- Learning to Approximate Uniform Facility Location via Graph Neural Networks
- Learning to Bet for Horizon-Aware Anytime-Valid Testing
- Learning to Correct: Reinforcement Learning for Multi-Attempt Chain-of-Thought
- Learning to Decode Against Compositional Hallucination in Video Multimodal Large Language Models
- Learning to Discover at Test Time
- Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
- Learning to Evict from Key-Value Cache
- Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
- Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
- Learning to Extrapolate to New Tasks: A Relational Approach to Task Extrapolation
- Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
- Learning to Listen: ICML 2026 Workshop on Machine Learning for Audio
- Learning-To-Measure: In-Context Active Feature Acquisition
- Learning to Memorize with Attributive and Associative Memory for Online Test-Time Adaptation of Vision-Language Models
- Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
- Learning-to-Optimize via Deep Unfolded Flows
- Learning to Perceive the World Through Control: Empowerment-Based Representation Learning
- Learning to Rank by Directly Optimizing Full-Order Probabilities
- Learning to Rank from Incomplete Rankings
- Learning to Reason for Factuality
- Learning to Reconfigure: Co-designing Reconfigurable robots for Heterogeneous Locomotion
- Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting
- Learning to Remember, Learn, and Forget in Attention-Based Models
- Learning to Route Languages for Multilingual Preference Optimization
- Learning to Search and Searching to Learn for Generalization in Planning
- Learning to Self-Verify Makes Language Models Better Reasoners
- Learning to Share: Selective Memory for Efficient Parallel Agentic Systems
- Learning to Theorize the World from Observation
- Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
- Learning to Watch: Active Video Anomaly Understanding via Interleaved Policy Optimization
- Learning to Watermark in the Latent Space of Generative Models
- Learning Transferable Interaction Primitives from Game Videos for Humanoids
- Learning Treatment Allocations with Risk Control Under Partial Identifiability
- Learning Treatment Representations for Downstream Instrumental Variable Regression
- Learning Unanimously Acceptable Lotteries via Queries
- Learning Unmasking Policies for Diffusion Language Models
- Learning Useful Supervision for Reinforcement Learning in Reasoning Models
- Learning What to Generate: A Reinforcement Learning-based Closed-Loop Augmentation Framework for Person Re-identification
- Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
- Learning When to Attend: Conditional Memory Access for Long-Context LLMs
- Learning with Admissibility: Robust Fuzzy Hashing for Cross-Modal Retrieval with Noisy Labels
- Learn to change the world: Multi-level reinforcement learning with model-changing actions
- Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-gated LLM
- Learn to Merge: Meta-Learning for Adaptive Multi-Task Model Merging
- Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
- Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
- LECTOR: Joint Learning of Scientific Reasoning Graphs and Introduction Generation
- Left–Right Symmetry Breaking in CLIP-style Vision-Language Models Trained on Synthetic Spatial-Relation Data
- LEGO: An LLM-Enabled Hierarchical Optimizer for Tensor Computation Graphs with Structure-Aware Search and Compositional Synthesis
- LEGO-FL: Learning Heterogeneous Federated Models as a LEGO Assembly Games
- LEMUR: Learned Multi-Vector Retrieval
- Length Generalization Bounds for Transformers
- LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
- Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models
- Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs
- Less Is More: Elevating RAG via Performance-Driven Context Compression
- Less Is More: Fast and Accurate Reasoning with Cross-Head Unified Sparse Attention
- Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure
- Less Is More in Federated Continual Learning: RieSelect for Conflict-Aware Layer Selection in LLMs
- Less is More: Neuroscience-Motivated Probing for Efficient Concept Circuits Tracing
- Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization’s Impact on VLMs Beyond Accuracy
- Less Token, More Signal: MoE Expert Pruning via Critical Token Selection
- Let EEG Models Learn EEG
- Let Language Constrain Geometry: Vision–Language Models as Semantic and Spatial Critics for 3D Generation
- Let the Prototype Guide You: Robust Aggregation of Sparse Multi-Class Annotations via Annotator Prototype Learning
- Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching
- Leveraging Evidence Priors for Robust Prompt Learning under Noisy Supervision in Vision-Language Models
- Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
- Leveraging Lineage Barcodes as Natural Augmentations for Contrastive Learning of Cell Fate in scRNA-seq Data
- Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
- Leveraging Machine Unlearning for Cost-Efficient Preference Alignment
- LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs
- Lie-Algebraic Neural Koopman Dynamics
- LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems
- LieWarper: Geometry-Aware Motion Transfer via Lie Algebra
- LIF Recurrent Memory Enables Long-Horizon Spiking Computation
- LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
- Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks
- LiftQuant: Continuous Bit-Width Control for Pareto-Optimal LLM Deployment
- LightAVSeg: Lightweight Audio-Visual Segmentation
- Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention
- LightningRL: Breaking the Accuracy–Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning
- Lightning Unified Video Editing via In-Context Sparse Attention
- Light Up Your Face: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement
- Lightweight and Interpretable Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast
- Lightweight Federated Incremental Learning via Decoupled Replay
- Likelihood Matching for Diffusion Models
- Likelihood over Estimation: Robust Quadratic Discriminant Analysis for Heavy-Tailed Distributions with Theory and Evidence
- LILO: Bayesian Optimization with Natural Language Feedback
- LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
- LIMMT: Less is More for Motion Tracking
- LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
- LiMuon: Light and Fast Muon Optimizer for Large Models
- LineageFlow: Flow Matching for High-Fidelity Family-Aware Protein Sequence Generation
- Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport
- Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
- Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
- Linear Ensembles Wash Away Watermarks: On the Fragility of Distributional Perturbations in LLMs
- Linearizing Vision Transformer with Test-Time Training
- Linear Regression with Unknown Truncation Beyond Gaussian Features
- Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG
- Linguistic Properties and Model Scale in Brain Encoding: From Small to Compressed Language Models
- Linguistic Relative Policy Optimization for Video Anomaly Reasoning
- Lions and Muons: Optimization via Stochastic Frank-Wolfe
- LipoPU: Pocket-level Prediction of Lipid-Protein Interactions via Positive-Unlabeled Learning
- Listening Through the Noise: Cauchy-Driven Diffusion Bridges for Robust Gastrointestinal Auscultation and Clinical Benchmarking
- LiteVSR: Enabling Cross-Domain Fine-Grained Detail Generation in Light-Weight Transformers for Video Super-Resolution
- LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithography
- LitReview Arena: Evaluating Literature Review Agents with Battle-style Peer Review Platform
- Little By Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
- LiveFigure: Generating Editable Scientific Illustration with VLM Agents
- LIVE: Long-horizon Interactive Video World Modeling
- LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News
- LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?
- LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
- LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior
- LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs
- LLM4Cov: Execution-Grounded Agent Learning for High-Coverage Hardware Verification
- LLM-based Embeddings: Attention Values Encode Sentence Semantics Better Than Hidden States
- LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
- LLM-Guided Diagnostic Evidence Alignment for Medical Vision–Language Pretraining under Limited Pairing
- LLM-Guided Loop Bound Generation for Program Termination Verification
- LLMInertia: Adaptive Counter-Inertial Reasoning to Improve Evidence Faithfulness in Large Language Models
- LLM-MatLogic: Executable Exchange Contracts for Knowledge-Graph Query Answering with Scoped Negation
- LLM Priors for ERM over Programs
- LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
- LLM Self-Recognition: Steering and Retrieving Activation Signatures
- LLM Watermark Evasion via Bias Inversion
- LMCleaner: Efficient and Certified Online Unlearning via Influence Propagation Truncation
- LMM4-IC4K: A Large Multimodal Model Powered Integrated Circuit Footprint Geometry Understanding
- LoBCD-GW: A Fast and Data-Dependent Algorithm for Computing Gromov-Wasserstein Distance via Localized Block Coordinate Descent
- LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth
- Local Constrained Bayesian Optimization
- Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
- Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation
- Local Intrinsic Dimension of Representations Predicts Alignment and Generalization in AI Models and Human Brain
- Localize and Neutralize: Gradient-Guided Token Suppression Against Visual Prompt Injection Attack
- Localized, High-resolution Geographic Representations with Slepian Functions
- Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences
- Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
- Locally Coherent Parallel Decoding in Diffusion Language Models
- Local MAP Sampling for Diffusion Models
- Local Mechanisms of Compositional Generalization
- Local Minima in Quadratic-Penalty Relaxations of Binary Linear Programs
- Local-Minima-Preserving Polynomial Relaxation of Ising Problems
- Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization
- LocalV: Exploiting Information Locality for IP-level Verilog Generation
- Locate then Correct: Debiasing Attention Heads in CLIP
- LoCoT2V-Bench: Benchmarking Long-Form and Complex Text-to-Video Generation
- Logarithmic Switching Regret for Online Convex Optimization
- Logical Guidance for the Exact Composition of Diffusion Models
- LogicSAGE: Neuro-Symbolic Reasoning with Socratic-Guided Enhancement
- Logit-Attention Divergence: Mitigating Position Bias in Multi-Image Retrieval via Attention-Guided Calibration
- Logit Distance Bounds Representational Similarity
- Log-Normal Multiplicative Dynamics for Stable Low-Precision Deep Learning
- LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
- Long-Context Modeling with Dynamic Hierarchical Sparse Attention for Memory-Constrained LLM Inference
- LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
- Long Grounded Thoughts: Synthesizing Grounded Visual Problems and Distilling Reasoning Chains at Scale
- Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism
- Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimization
- Long-term Fairness with Selective Labels
- Lookahead-GCG: Improving Multi-Model Gradient-Based Jailbreaking Attacks via Nesterov Momentum
- Lookahead Path Likelihood Optimization for Diffusion LLMs
- Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
- Lookahead Unmasking Elicits Reliable Decoding in Diffusion Language Models
- Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation
- Look on Demand: A Cognitive Scheduling Framework for Visual Evidence Acquisition in Multimodal Reasoning
- LoPhyDA: Low-Rank Tensor and Physics Gradient Guided Diffusion for Atmospheric Data Assimilation
- LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis
- LORD-GoF: A Robust Online Detection Approach for LLM Watermarks in Sparse and Mixed Streams
- LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
- LoRe: Adaptive Interaction-Evaluation Routing with Per-step Interaction Budgets for Iterative Graph Solvers
- LoSA: Locality Aware Sparse Attention in Diffusion Language Models
- Loss-aware distributionally robust optimization via trainable optimal transport ambiguity sets
- Lost in Context: Discovering Context Anxiety in Large Language Models
- LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
- Lottery Prior: Randomized Neural Compression for Zero-Shot Inverse Problems
- LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation
- Low-Compute Watermark Removal via Dual-Domain Natural Projection
- Low-cost Full Fine-tuning: Learning What to Update for LLMs
- Low-dimensional topology of deep neural networks
- Lower Bounds for Frank-Wolfe on Strongly Convex Sets
- Lower Complexity Bounds for Nonconvex-Strongly-Convex Bilevel Optimization with First-Order Oracles
- Low Kruskal-Rank Adaptation
- Low-Rank and Sparsity Are All You Need: Exploring Robust Hierarchical Latent Subspaces for Transferable Adversarial Attack
- LOZO+: Provably Efficient Zeroth-Order Fine-Tuning via Greedy Low-Rank Subspace Selection
- LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents
- LS$^{2}$MC-GDA: A Smoothed Algorithm for Federated Stochastic Compositional Minimax Optimization
- LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution
- L-SR1: Learned Symmetric-Rank-One Preconditioning
- LUCID: Attention with Preconditioned Representations
- LUGS: Latent-aware Guidance for Efficient Unmasking in Diffusion Large Language Models
- LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
- LynX: Token Interface Alignment for Video+X LLMs
- MA$^3$S: Model-Agnostic Active Annotation Strategy for Crowdsourcing
- MACD: Model-Aware Contrastive Decoding via Counterfactual Data for Video-LLMs
- Machine Learning Hamiltonians are Accurate Energy-Force Predictors
- MAC-NeRF: Motion-Aware Curriculum Learning for Dynamic LiDAR NeRFs
- MacroGuide: Topological Guidance for Macrocycle Generation
- MADA-Attack: Transferable Multi-modal Attention Distraction Adversarial Attack against Vision Language Models
- M+Adam: Low-Precision Training via Mantissa–Exponent Optimization
- MADE: Benchmark Environments for Closed-Loop Materials Discovery
- MAD: Manifold Attracted Diffusion
- MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems
- MAGIC: A Co-Evolving Attacker–Defender Adversarial Game for Robust LLM Safety
- MAGIC: Multi-Granularity Language-Informed Image Clustering
- Magnitude Distance: A Geometric Measure of Dataset Similarity
- Making Foundation Models Probabilistic via Singular Value Ensembles
- Making Learner Weakness Actionable for Learning from Demonstration with Novice Teachers
- Making Models Unmergeable via Scaling-Sensitive Loss Landscape
- MALICE: Memory-aware Loop Invariants Generation on Symbolic Execution Traces
- MalTree: Tracing Malware Evolution using Embeddings at Scale
- MaMa: A Game-Theoretic Approach for Designing Safe Agentic Systems
- MAMBO-G: Magnitude-Aware Mitigation for Boosted Guidance
- MaMi-HOI: Harmonizing Global Kinematics and Local Geometry for Human-Object Interaction Generation
- MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation
- Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution
- Manifold-Aware Perturbations for Constrained Generative Modeling
- ManifoldKV: Training-Free KV Cache Compression via Euclidean Outlier Detection
- Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance
- ManiSoft: Towards Vision-Language Manipulation for Soft Robotics
- Mantis: Lightweight Foundation Model for Time Series Classification
- Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
- Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments
- Many-Shot CoT-ICL: Making In-Context Learning Truly Learn
- MapDream: Task-Driven Map Learning for Vision-Language Navigation
- MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Models Serving
- MapUQ: Map with Uncertainty Quantification for Robust BEV Vectorized Construction
- Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
- MarketSim: Simulating Stock Markets with Large-Scale Generative Agents
- Markov Chain Monte Carlo without Evaluating the Target: an Auxiliary Variable Approach
- Markovian Projection of Star-Shaped Diffusion for Exponential Family Distributions
- Marrying Generative Model of Healthcare Events with Digital Twin of Human-Environment Interaction for Disease Reasoning
- MARS: Modular Agent with Reflective Search for Automated AI Research
- MARS-SQL: A Multi-Agent Reinforcement Learning Framework For Text-To-SQL
- MAS-Architect: Declarative Multi-Agent System Design via Separation of Concerns
- MASH: Modeling Abstention via Selective Help-Seeking
- Masked Multi-path Contrast with Confidence-Gated Semantic Imputation for Incomplete Multi-view Clustering
- Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models
- MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks
- MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks
- MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
- MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems
- MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
- MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics
- Matrix-Free GPU Semidefinite Programming for Quantum Ordered Search at the k=6 Frontier
- Matroid Algorithms Under Size-Sensitive Independence Oracles
- Maximin Relative Improvement: Fair Learning as a Bargaining Problem
- Maximizing mutual information between prompt and response improves LLM performance with no additional data
- Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
- Maximum-Likelihood Learning of Latent Dynamics Without Reconstruction
- Maximum Likelihood Reinforcement Learning
- MaxSAT-Based Compression for Tsetlin Machines
- MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning
- MC-HNN: Learning Latent Structural Semantics and High-Rank Representations for Hypergraph Neural Networks
- MCP-Persona: Benchmarking LLM Agents on Personalized MCP Tools and Tasks
- MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
- MDN: Parallelizing Stepwise Momentum for Delta Linear Attention
- MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning
- Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models
- Mean Flow Policy Optimization
- Mean-Shift PCA by Knockoff Mean
- Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
- Measuring and Mitigating Post-hoc Rationalization in Reverse Chain-of-Thought Generation
- Measuring Intent Comprehension in LLMs
- Measuring Meta-Cultural Competency: A Spectral Framework for LLM Knowledge Structures
- MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
- Mechanisms of Introspective Awareness
- Mechanistic Anomaly Detection via Functional Attribution
- Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units
- Mechanistic Interpretability as Statistical Estimation: A Variance Analysis
- MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding
- MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
- MEDA: Medical-Oriented Activation Editing for Hallucination Mitigation in Medical Large Vision-Language Model
- MedCoG: Maximizing LLM Inference Density in Medical Reasoning via Meta-Cognitive Regulation
- MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery
- MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
- MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio
- MedREK: Retrieval-Based Editing for Medical LLMs with Key-Aware Prompts
- MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling
- Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
- Med-SegLens: Latent-Level Model Diffing for Interpretable Medical Image Segmentation
- MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models
- MEDUSA: Motion Elimination in Diffusion Using Spectral Attack
- Meerkat-VL: Implicit Risk Safety Alignment in Multimodal LLMs via Perceptual Reasoning and Self-Verification
- MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
- Membership Inference Attacks for Unseen Classes
- MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
- MemDecoder: Enhancing Test-Time Compute for LLM Agents via Reinforced Memory Decoding
- MemEvolve: Meta-Evolution of Agent Memory Systems
- MemIncept: Steering LLM Agents via Cooperative Stealthy Memory Injections
- MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning
- MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
- Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity
- Memoria-Bench: A Comprehensive Benchmark for Evaluating Memory in Long-Horizon Autonomous Agents
- Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping
- Memory as Dynamics: Learning Reliability-Guided Predictive Models for Online Video Perception
- MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
- Memory Caching: RNNs with Growing Memory
- Memory-Distilled Selection for Noise-Robust Anomaly Detection
- Memory-Efficient LLM Pretraining via Minimalist Optimizer Design
- Memory-Efficient LLMs Training with Dynamic Sparsity: From Stability to Practical Scaling
- Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents
- MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
- Memory Savings at What Cost? A Study of Alternatives to Backpropagation
- *MemPot*: Defend Against Memory Extraction Attack with Optimized Honeypots
- Mem-T: Densifying Rewards for Long-Horizon Memory Agents
- MentisOculi: Revealing the Limits of Reasoning with Mental Imagery
- MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
- MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning
- MER-DG: Modality-Entropy Regularization for Multimodal Domain Generalization
- MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
- Merge to Remember: Sharpness-Aware Isotropic Merging for Continual Learning
- MESA: Improving MoE Safety Alignment via Decentralized Expertise
- Mesh Based Simulations with Spatial and Temporal awareness
- Mesh Field Theory: Port–Hamiltonian Formulation of Mesh-Based Physics
- MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
- Message Passing on the Edge: Towards Scalable and Expressive GNNs
- Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
- MetaBio: Learning from metadata for bioacoustics foundation models
- Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
- Meta Context Engineering via Agentic Skill Evolution
- MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Metadynamics
- Meta Flow Maps enable scalable reward alignment
- Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments
- Meta-learning Structure-Preserving Dynamics
- MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
- MetaOthello: A Controlled Study of Multiple World Models in Transformers
- MetaphorVU: Towards Metaphorical Video Understanding
- MetaStreet: Semi-Supervised Multimodal Learning for Street-Level Socioeconomic Prediction
- MET-Bench: Multimodal Entity Tracking for Evaluating the Limitations of Vision-Language and Reasoning Models
- Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization
- Metric–-Phase Fields: Decoupling Distance and Sign for Thin-Structure Reconstruction from Unoriented Point Clouds
- MFCL Audio: An Audio Function Calling Evaluation for Large Language Models
- MFH-NAS:A Hybrid Neural Architecture Search Framework for Multimodal Fusion Object Detection
- MGAL: A Multilingual Granularity-Aware Long-Context Benchmark
- mHC: Manifold-Constrained Hyper-Connections
- MICE-Bench: A Challenging and Comprehensive Benchmark for Multi-Reference Image Creation and Editing
- M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
- MIDSTEER: Optimal Affine Framework for Steering Generative Models
- Midtraining Bridges Pretraining and Posttraining Distributions
- Milestone-Guided Policy Learning for Long-Horizon Language Agents
- MIMO-LP: A Multi-Input Multi-Output Framework for Subgraph-based Link Prediction
- MIMOMamba: From Scalar Duality to Matrix-Valued Attention
- MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement
- Mind Dreamer: Untethering Imagination via Active Counterfactual Reasoning on Latent Manifolds
- MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation
- MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models
- Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion
- Mind the budget: Accelerating Deep Reinforcement Learning using Early Exit Neural Networks
- Mind the Gap: Catching Hallucinations via Evidence Drop on the Reasoning Manifold
- Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
- Mind the Gap: Structure-Aware Consistency in Preference Learning
- Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
- Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
- MindZero: Learning Online Mental Reasoning With Zero Annotations
- MineDraft: A Framework for Batch Parallel Speculative Decoding
- MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants
- Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
- Minibatch selection for Language Models via Partition Matroid Constrained Gradient Matching
- MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification
- MiniMax Learning of Interpretable Factored Stochastic Policies from Conjoint Data, with Uncertainty Quantification
- Minimax-Optimal Policy Regret in Partially Observable Markov Games
- Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
- Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection
- Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
- MINIM: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization
- Minimum Distance Summaries for Robust Neural Posterior Estimation
- Mining Tensor/Neuron-Level Sparsity to Maximize Mixture-of-Experts Potential in Post-Training and Inference
- Mining Useful General Data for Low-Resource Domain Adaptation
- MiniX: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
- MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
- MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
- MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency
- Mirror Descent Actor Critic via Bounded Advantage Learning
- Mirror Descent Under Generalized Smoothness
- Mirror Mean-Field Langevin Dynamics
- MIST: Moment-Aligned Invariant Stability Transform for Robust Flow Matching
- Mitigating Bias in Locally Constrained Decoding via Tractable Proposals
- Mitigating Conversational Inertia in Multi-Turn Agents
- Mitigating Error Accumulation in Continuous Navigation via Memory-Augmented Kalman Filtering
- Mitigating Error Propagation in Low-Rank Approximation of Large Models via Distribution-Aware Whitening
- Mitigating Gradient Pathology in PINNs through Aligned Constraint
- Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating
- Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment
- Mitigating Manifold Departure: Uncertainty-aware Subspace Rectification for Trustworthy MLLM Decoding
- Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models
- Mitigating Noise-Induced Layout Priors for Object Counting in Diffusion Models
- Mitigating Perceptual Judgment Bias in Multimodal LLM-as-a-Judge via Perceptual Perturbation and Reward Modeling
- Mitigating Per-Sample Harm in Stochastic Optimization
- Mitigating Plasticity Loss through Architectural Design in Continual Learning
- Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
- Mitigating Reward Hacking in LLM-based Recommendation: A Preference Optimization Approach
- Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling
- Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
- Mitigating Surgical Data Imbalance with Dual-Prediction Video Diffusion Model
- Mitigating the Contractivity Trap in Diffusion ODEs via Stein Stabilization
- Mitigating the Modality Gap in Vision–Language Models with Fractal Spectral Geometry
- Mitigating the Safety–Utility Trade-off in LLM Alignment via Adaptive Safe Context Learning
- Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck
- Mitigating Visual Hallucinations via Semantic Curriculum Preference Optimization in MLLMs
- MiVE: Multiscale Vision-language features for reference-guided video Editing
- MixFP4: Extending NVFP4 to Mixed Micro-Format via Scale-Bit Reuse and Tensor Core Co-design
- Mixing Configurations for Downstream Prediction
- Mixing Expertise with Confidence: A Mixture of Expert Framework for Robust Multi-Modal Continual Learner
- MixQuant: Pushing the Limits of Block Rotations in Post-Training Quantization
- MixReasoning: Switching Modes to Think
- Mixture of Concept Bottleneck Experts
- Mixture of Distributions Matters: Dynamic Sparse Attention for Efficient Video Diffusion Transformers
- Mixture of Horizons in Action Chunking
- Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
- Mixtures Closest To A Given Measure: A Semidefinite Programming Approach
- Mixtures of geodesic factor analyzers on Riemannian homogeneous spaces
- ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
- ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World
- MLLM-4D: Towards Visual-based Spatial-Temporal Intelligence
- MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
- MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models
- mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
- MMClima: A Framework for Multimodal Climate Science Data and Evaluation
- MM-DeepResearch: A Simple and Effective Multimodal Agentic Search Baseline
- MME-Reasoning: A Broad-Spectrum Benchmark for Evaluating Logical Reasoning in MLLMs
- MMKU-Bench: A Multimodal Update Benchmark for Diverse Visual Knowledge
- MMPD-Bench: Bridging Multimodal Fission with Multi-Polarimetric Modalities Decomposition
- MM-Snowball: Evaluating and Mitigating Hallucination Snowballing in Multimodal Multi-turn Dialogue
- MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework
- MN-Diff: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
- MobileFusion: Mobile-Friendly Infrared and Visible Image Fusion via Structural Re-parameterization
- Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement
- MoCL: Metabolic Optimization for Curvature-Aware Continual Learning
- MOC: Multi-Order Communication in LLM-based Multi-Agent Systems
- MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks
- Modality-Decoupled Online Recursive Editing
- MoDA: Modulation Adapter for Fine-Grained Visual Understanding in Instructional MLLMs
- Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making
- Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models
- Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis
- Model Fusion via Retrofitting
- Modeling Attributional Style at Scale: A Dataset and Analysis for Psychological Attribution Assessment and Reframing
- Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
- Modeling Hierarchical Thinking in Large Reasoning Models
- Modeling Long-Tail Relations in the Operating Room via In-Context Multimodal Learning
- Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
- Modeling temporal scRNA-seq data with latent Gaussian process and optimal transport
- Modelling Attention with Aitchison Geometry: Token Distinguishability and Temperature Scaling
- MODEL MERGING SCALING LAWS IN LARGE LANGUAGE MODELS
- Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
- Model-Preserving Adaptive Rounding
- MODEL SOUPS NEED ONLY ONE INGREDIENT
- Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
- ModernVBERT: Towards Smaller Visual Document Retrievers
- Mode Seeking meets Mean Seeking for Long Video Generation
- MOD-SR: Unifying Multimodal Learning and Direct Optimization with Gradient-Guided Diffusion Model for Symbolic Regression
- Modular Pretraining Enables Access Control
- MODUS: Decoder-only Any-to-Any Modeling of Diverse Modalities
- MOES-Pred: Molecular Structural Representation Learning by Adaptive Energy-Sentinel Vibration for Generalized Property Prediction
- MolAlign3D: Enhancing Fixed-Dimensional E(3)-Equivariant Latent Space for High-Fidelity 3D Molecular Reconstruction and Editing
- MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology
- MoLoRA: Composable Specialization via Per-Token Adapter Routing
- Moment Matching Q-Learning
- Momentum Further Constrains Sharpness at the Edge of Stochastic Stability
- Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoning
- Monitoring LLM-based Multi-Agent Systems Against Corruptions via Node Evaluation
- Monitoring Monitorability
- MonoScale: Scaling Multi-Agent System with Monotonic Improvement
- Monotonic Variational Gaussian Process for Efficient Data Collection
- MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier
- MORALISE: A Structured Benchmark for Moral Alignment in Visual Language Models
- MORE: A Multilingual Document Parsing Benchmark and Evaluation
- More Capable, Less Cooperative? When LLMs Fail at Zero-Cost Collaboration
- More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing
- More Sail than Ballast: Addressing Harmful Knowledge Leakage in the Expansive Reasoning Space of LRMs
- MoRGEN: Mixture-of-Resolutions Generative Forecasting for Irregularly Sampled Medical Time-Series Data
- Mosaic: Runtime-Efficient Multi-Agent Embodied Planning
- Mosaic: Unlocking Over 30$\times$ Context Length for Diffusion LLMs Inference via Global Memory Planning and Dynamic Peak Taming
- MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy
- MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
- MoshiRAG: Asynchronous Knowledge Retrieval for Full-Duplex Speech Language Models
- MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method for Nonconvex Constrained DC Optimization
- MoST: Mixing Speech and Text with Modality-Aware Mixture of Experts
- MotiMotion: Motion-Controlled Video Generation with Visual Reasoning
- Motion Attribution for Video Generation
- Motion-Aware Caching for Efficient Autoregressive Video Generation
- Motion Dynamics Learning for Few-Shot Embodied Adaptation
- MotionGRPO: Overcoming Low Intra-Group Diversity in GRPO-Based Egocentric Motion Recovery
- MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations
- Motion Planning in Compressed Representation Spaces
- Motion-Residual Conflict-Aware Time Reversal for Generative Inbetweening
- Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation
- MoVie: Multimodal Video Compression with Text Guidance
- Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision
- Moving Out: Physically-grounded Human-AI Collaboration
- MPFM: Cross Multi-Domain Prototype Flow Matching for Log Anomaly Detection
- MRPO: Magnitude-Regularized Policy Optimization via L1 Constraints
- MSP: Probabilistically Consistent Multi-Scale Action Generation
- MTNL: A Unified Modeling Perspective for Enhancing Tensor Network Learning
- MuCO: Generative Peptide Cyclization Empowered by Multi-stage Conformation Optimization
- MulFCoder: Framework-conditioned Multi-agent for MLLM-based Multi-framework Front-end Code Generation
- MuLoCo: Muon is a Practical Inner Optimizer for DiLoCo
- Multi-Adapter Representation Interventions via Energy Calibration
- Multi-agent imitation learning with function approximation: linear Markov games and beyond
- Multi-Agent Reinforcement Learning with Submodular Reward
- Multi-Agent Teams Hold Experts Back
- MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety
- Multicalibration Yields Better Matchings
- Multi-Distribution Robust Conformal Prediction
- MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
- Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
- Multi-Head LatentMoE and Head Parallel: Communication-Efficient and Deterministic MoE Parallelism
- Multi-Integration of Labels across Categories for Component Identification (MILCCI)
- Multi-label learning with contrastive cluster self-supervision for 3D hierarchical semantic segmentation
- Multi-Label Test-Time Adaptation with Bayesian Conditional Priors
- Multi-Level Strategic Classification: Incentivizing Improvement through Promotion and Relegation Dynamics
- Multilingual Safety Alignment via Representation-Space Separability
- Multilingual Safety Alignment Via Sparse Weight Editing
- Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility
- MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
- Multimarginal flow matching with optimal transport potentials
- Multi-marginal temporal Schrödinger Bridge Matching from unpaired data
- Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling
- Multimodal Function Vectors for Spatial Relations
- Multimodal Fusion via Self-Consistent Task-Gradient Fields
- Multimodal Latent Language Modeling with Next-Token Diffusion
- Multimodal Meta-Verifier with Explicit Structured Recalibration
- Multimodal Nested Learning for Decoupled and Coordinated Optimization
- Multimodal Scaling Laws for Task & Data-Optimized Models of Visual Cortex
- Multi-Objective Bayesian Optimization via Adaptive $\varepsilon$-Constraint Decomposition
- Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
- Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
- Multi-Objective Protein Design via Memory-Aware Test-Time Scaling in Diffusion Models
- Multiple Choice Learning of Low-Rank Adapters for Language Modeling
- Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining
- MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models
- Multi-Round Human–AI Collaboration with User-Specified Requirements
- Multi-scale Explainer for Graph Neural Networks
- Multi-Scale Wavelet Transformers for Operator Learning of Dynamical Systems
- Multi-Task Bayesian In-Context Learning
- Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
- Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness and Safety
- Multi-timescale Reinforcement Learning by Value Reconstruction
- Multivariate distributional reinforcement learning using sliced divergences
- Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
- Multi-view Consistent Latent Action Learning for World Modeling and Control
- Multiview Self-Representation Learning across Heterogeneous Views
- Multi-Way Representation Alignment
- Muon in Associative Memory Learning: Training Dynamics and Scaling Laws
- MuonSSM: Orthogonalizing State Space Models for Sequence Modeling
- MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
- MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality
- MusicDET: Zero-Shot AI-Generated Music Detection
- Must All Negatives Be Pushed Away Equally? Uncertainty-Aware Cross-View Geo-Localization via Normal Inverse Gamma Distribution
- MutAtlas: A PDB-Wide Energy-Guided Atlas of Protein Mutation Effects
- MV-FGAD: Towards Efficient and Effective Federated Graph Anomaly Detection via Multi-view Learning
- MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
- MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation
- MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
- MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation
- N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
- NAACA: Training-Free NeuroAuditory Attentive Cognitive Architecture with Oscillatory Working Memory for Salience-Driven Attention Gating
- Names Don’t Matter: Symbol-Invariant Transformer for Open-Vocabulary Learning
- NanoFLUX: Distillation-Driven Compression of Large Text-to-Image Generation Models for Mobile Devices
- NanoQuant: Efficient Sub-1-bit Quantization of Large Language Models
- NanoSpec: Accelerating Speculative Decoding using Minimalist In-Context Vocabularies
- NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs
- Narrowing the ANN–SNN Gap for 1D Signal Classification with Multi-Scale Temporal Encoding and Sparsity-Regularized Transform Encoding
- Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation
- Native Active Perception as Reasoning for Omni-Modal Understanding
- Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
- Native Spatio-Temporal 4D Variational Autoencoder
- Natural Hypergradient Descent: Algorithm Design, Convergence Analysis, and Parallel Implementation
- Natural Language Actor–Critic Is Bilevel: Learning to Reason with Textual Feedback
- NaviAgent: Graph‑Driven Bilevel Planning for Scalable Tool Orchestration
- NaviCache: Test-Time Self-Calibration Caching for Video Generation
- NAVIGATE: Evaluating Visual-Guided Search Decision-Making on the Open Web
- Navigating the Energy Landscape of Collaboration: Multi-Agent Communication Graph Generation via Score-Based Diffusion
- Navigating the Flatlands: Dual Adaptive Sharpness-Aware Minimization for Domain Generalization
- Navigating the Pareto Frontier of Alignment:Spectrum-Adaptive Fine-Tuning for LLMs
- NavOL: Navigation Policy with Online Imitation Learning
- NBCG: Nash-Bargained Causal Game for Long-Tailed Multi-Label NLP
- nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding
- Near-Minimax Multi-Objective RL under Predictable Adversarial Preferences and Preference-Free Exploration in Linear MDPs
- Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
- Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and $(L_0,L_1)$-Smoothness
- Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantation
- Near-Optimal Private Linear Regression via Iterative Hessian Mixing
- Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
- Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
- Near-Universal Multiplicative Updates for Nonnegative Einsum Factorization
- Necessary Conditions for Compositional Generalization of Embedding Models
- Needles in the Haystack: Addressing Signal Dilution Improves scRNA-seq Perturbation Response Modeling and Evaluation
- Negative Sampling From the Ground Up: A Redesign for Graph-based Recommendations
- Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains
- NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
- Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution
- Nested Spatio-Temporal Time Series Forecasting
- NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies
- Networked Information Aggregation for Binary Classification
- NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs
- Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models
- Neural Collapse by Design: Learning Class Prototypes on the Hypersphere
- Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings
- Neural Control: Adjoint Learning Through Equilibrium Constraints
- Neural Dispersion on Graphs
- Neural–Evolutionary Symbolic Regression with Global Constraints: Constraint-Aware Decoding and Reward Shaping
- Neural Feature Geometry Evolves as Discrete Ricci Flow
- NeuralFLoC: Neural Flow-Based Joint Registration and Clustering of Functional Data
- Neural Honeytrace: Plug&Play Watermarking Framework against Model Extraction Attacks
- Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
- Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models
- Neural-Inspired Modeling of Auditory Selection and Compensation for Audio-Visual Speech Separation
- Neural Logistic Bandits
- Neural Low-Discrepancy Sequences
- Neural Minimum Weight Perfect Matching for Quantum Error Codes
- Neural Modular Physics for Elastic Simulation
- Neural QAOA$^2$: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization
- Neural Quantum States in Mixed Precision
- Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
- Neural Vector Lyapunov–Razumikhin Certificates for Delayed Interconnected Systems
- NeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding
- NeuroCLUS: A Foundation Model with Functional Clustering for Intracranial Neural Decoding
- NeurOCNN: A Neural-Operator-Based Model for Physiological Time Series
- Neuro-evolutionary Continual Reinforcement Learning
- Neuro-Fuzzy Concept Learning for Interpretable Large Multimodal Models
- NeuroMamba: A Universal Spatiotemporal Module for Robust Perception in Degraded Sensory Streams
- Neuromem: A Granular Decomposition of the Streaming Lifecycle in External Memory for LLMs
- NeuronCtrl: Geometry-Aware Safe Closed-Loop Generative Control for Neuronal Microenvironment Dynamics
- Neuro-Symbolic AI for Analytical Solutions of Differential Equations
- NeurVLA: Unleashing Failure-Handling Capability of Vision-Language-Action Models via Neural-Symbolic Reasoning
- Neutral-Reference Prompting for Vision–Language Models
- New Algorithms for Fully-Dynamic k-center with Outliers
- New Bounds for Kernel Sums via Fast Spherical Embeddings
- New Frontiers in Game-Theoretic Learning
- Newton-coupled Dual-Teacher Semi-supervised Learning Framework
- New Wide-Net-Casting Jailbreak Attacks Risk Large Models
- Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense
- NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels
- Next-Token Prediction and Regret Minimization
- NITP: Next Implicit Token Prediction for LLM Pre-training
- NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
- NNiT: Width-Agnostic Neural Network Generation with Structurally Aligned Weight Spaces
- No Data? No Problem: Robust Vision-Tabular Learning with Missing Values
- No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
- No Global Plan in Sight: Uncover the Myopic Planning Horizon of LLMs
- Noise as a Natural Regularizer in Markov Decision Processes: Connecting Environmental Stochasticity and Policy Simplicity
- Noise-corrected GRPO: From Noisy Rewards to Unbiased Gradients
- Noise-Guided Transport: Imitation Learning from Random Priors
- Noise-Robust Density Estimation for Tabular Data Anomaly Detection
- NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision
- Noise Tectonics: Measuring the Stability of AI Benchmark Ecosystems
- Noisy-Channel Minimum Bayes Risk Decoding
- Noisy Pairwise-Comparison Random Search for Smooth Nonconvex Optimization
- Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning
- NOMAD: Lifelong Trajectory Planning via Non-Parametric Bayesian Memory-Adaptive Diffusion Experts
- No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
- No More, No Less: Least-Privilege Language Models
- Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Role of Bellman Constraints
- Nonconvex Low-Rank Tensor Representation with Deep Priors for Multiview Subspace Clustering
- No Need to Train Your RDB Foundation Model
- Non-Euclidean Gradient Descent Operates at the Edge of Stability
- Nonlinear Covariate Balance in Experimental Design
- Non-Monotonic Autoregressive Sequence Model
- Nonparametric Data Attribution for Diffusion Models
- Nonparametric Distribution Regression Re-calibration
- Nonparametric LLM Evaluation from Preference Data
- Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems
- Non-Parametric Probabilistic Robustness: A Conservative Risk Estimator under Unknown Perturbation Distributions
- Non-Stationary Online Structured Prediction with Surrogate Losses
- Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
- NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
- No Retraining at Edge: Efficient Resource-Aware Mixed-Precision Quantization via Federated Supernet Learning
- Norm$\times$Direction: Restoring the Missing Query Norm in Vision Linear Attention
- Normality Calibration in Semi-supervised Graph Anomaly Detection
- Normalization Equivariance for Arbitrary Backbones, with Application to Image Denoising
- Normalization-equivariant Diffusion Models: Learning Posterior Samplers From Noisy And Partial Measurements
- Normalized Energy Models for Linear Inverse Problems
- Normalized Rewards for Preference Optimization
- Normalizing Diffusion Kernels with Optimal Transport
- Normalizing Flows with Iterative Denoising
- Normative alignment: A new paradigm for principled autonomous agents
- NorMuon: Making Muon more efficient and scalable
- Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
- Not All Frequencies Are Equal: Energy-Adaptive Diffusion for Time Series Forecasting
- Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
- Not All Prefills Are Equal: PPD Disaggregation for Multi-turn LLM Serving
- Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematics
- OBCache: Optimal Brain KV Cache Pruning for Efficient Long-Context LLM Inference
- Object-level Semantic and Spatial Distillation for Open Vocabulary Detection
- ObjEmbed: Towards Universal Multimodal Object Embeddings
- OBJVanish: Prompt-Driven Generation of Physically Realizable 3D LiDAR-Invisible Objects
- Obliviate: Efficient Unlearning in Recommender Systems
- OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation
- OCNR: Stabilizing Self-Play by Mitigating Iteration-Collapse With One-Class Novelty Rewards
- OC-space: a Unifying Perspective on Verification of Tree Ensembles
- Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
- Offline Multi-Agent Reinforcement Learning via Sequential Score Decomposition
- Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs
- Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment
- Offline Reinforcement Learning with Generative Trajectory Policies
- Offline Reinforcement Learning with Universal Horizon Models
- Offline Two-Player Zero-Sum Markov Games with KL Regularization
- Off-Policy Evaluation Beyond Overlap under Network Interference
- Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random
- Off-Policy Evaluation with Strategic Agents via Local Disclosure
- Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
- Olaf-World: Orienting Latent Actions for Video World Modeling
- Old Habits Die Hard: How Conversational History Geometrically Traps LLMs
- OLion: Approaching the Hadamard Ideal by Intersecting Spectral and L inf Implicit Biases
- Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density
- Olmix: A Framework for Data Mixing Throughout LM Development
- OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
- Omitted Variable Bias in Language Models Under Distribution Shift
- OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild
- OmniDenseCap: Scripting Multi-Scene Videos with Time-Aware and Structural Audio-Visual Captions
- OmniFit: Bridging Modalities via Layer-Adaptive Token Compression for Omnimodal Large Language Models
- Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model
- OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
- Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
- OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization
- OmniShow: Orchestrating Multimodal Conditions for Human-Object Interaction Video Generation
- OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
- OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention
- OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL
- OMP: One-step Meanflow Policy with Directional Alignment
- Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation
- On Computation and Reinforcement Learning
- On Contraction of Sequential and Offset Rademacher Complexities
- On Densest $k$-Subgraph Mining and Diagonal Loading: Optimization Landscape and Finite-Step Exact Convergence Analysis
- One Batch Is Enough: A Unified Dataset Condensation Framework for General Time Series Analysis
- One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models
- One Bug, Hundreds Behind: LLMs for Large-Scale Bug Discovery
- One Coin Has Two Sides: Single Poistive Multi Label Learning from Salient Annotations
- On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
- On Efficient Scaling of GNNs via IO-Aware Layers Implementations
- One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State
- One LR Doesn’t Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs
- One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception
- OnePO: Direct One-stage Policy Optimization for SFT-free Domain Adaptation
- OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
- One-shot Conditional Sampling: MMD meets Nearest Neighbors
- One-shot Entropy Minimization for Language Model Reasoning
- One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data
- One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models
- One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining
- One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
- One-step Latent-free Image Generation with Pixel Mean Flows
- One-step Optimal Transport via Regularized Distribution Matching Distillation
- One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation
- One Tool Is Enough: Reinforcement Learning of LLM Agents for Repository-Level Code Navigation
- One-Way Policy Optimization for Self-Evolving LLMs
- On Expressive Power of Floating-Point Transformers
- On Group Relative Policy Optimization Collapse in Agent Search: The Lazy Likelihood-Displacement
- On Information Self-Locking in Reinforcement Learning for Active Reasoning
- On Learnability and Disambiguation of Multiclass Partial Concept Classes
- Online Bayesian Experimental Design for Partially Observed Dynamical Systems
- Online Change Point Detection for Multivariate Inhomogeneous Poisson Processes Time Series
- Online Compatible Reward Identification from Preference Feedback
- Online Conformal Prediction via Universal Portfolio Algorithms
- Online Continual Learning with Dynamic Label Hierarchies
- Online Contract Design With Unknown Technology
- Online Fair Division with Additional Information
- Online Learning and Inference for Cox Proportional Hazards Model Using Renewable Sieve Estimation
- Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
- Online Linear Programming for Multi-Objective Routing in LLM Serving
- Online Packet Scheduling with Deadlines and Learning
- Online Robust Reinforcement Learning with General Function Approximation
- Online Rubrics Elicitation from Pairwise Comparisons
- Online Social Welfare Function-based Resource Allocation
- On Local Policies for Graph-Structured Markov Decision Processes
- On Minimum Depth and Width of Floating-Point Neural Networks for Representing Floating-Point Functions
- On Multi-Step Theorem Prediction via Non-Parametric Structural Priors
- On Path to Multimodal Historical Reasoning: HistBench and HistAgent
- On Regret Bounds of Thompson Sampling for Bayesian Optimization
- On Revisiting Entropy for Identifying Mislabeled Medical Images
- On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
- On Stable Long-Form Generation: Benchmarking and Mitigating Length Volatility
- On Structured State-Space Duality
- On Testing Conditional Mean Independence for Manifold-Valued Data
- On the Ability of Transformers to Verify Plans
- On the Accuracy of Newton Step and Influence Function Data Attributions
- On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
- On the Anisotropy of Score-Based Generative Models
- On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering
- On the Computational Complexity of Performative Prediction
- On the Convergence of Decentralized Stochastic Minimax Optimization Algorithm with Compressed Communication
- On the Convergence of Steepest Descent and Adaptive Gradient Methods under Non-Uniform Smoothness
- On the Convergence Rate of LoRA Gradient Descent
- On the Coordination of Value-Maximizing Bidders
- On the Difficulty of Learning a Meta-network for Training Data Selection
- On the Effect of Misspecifying the Embedding Dimension in Low-rank Network Models
- On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
- On the Epistemic Uncertainty of Overparametrized Neural Networks
- On the existence of consistent adversarial attacks in high-dimensional linear classification
- On the Expressive Power of GNNs to Solve Linear SDPs
- On the Expressive Power of Permutation-Equivariant Weight-Space Networks
- On the Fragility of Data Attribution When Learning Is Distributed
- On the Generalization Gap in Self-Evolving Language Model Reasoning
- On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching
- On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding
- On the "Induction Bias" in Sequence Models
- On the Infinite Width and Depth Limits of Predictive Coding Networks
- On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach
- On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
- On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning
- On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
- On the Learning Dynamics of RLVR at the Edge of Competence
- On the Limits of LLM Adaptability: Impact of LLM Pre-Training on Annotation Task Performance
- On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference
- On the Optimization Trajectory of DeepWalk Embeddings
- On the origin of neural scaling laws: from random graphs to natural language
- On the Plasticity and Stability for Post-Training Large Language Models
- On the Power of (Approximate) Reward Models for Inference-Time Scaling
- On the Power of Source Screening for Learning Shared Feature Extractors
- On the Power of Statistics in Class-Incremental Learning with Pretrained Models
- On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
- On the Relationship Between Activation Outliers and Feature Death in Sparse Autoencoders
- On the Robustness of Langevin Dynamics to Score Function Error
- On the Role of Batch Size in Stochastic Conditional Gradient Methods
- On the Salience of Low-Probability Tokens for AI-Generated Text Detection: A Multiscale Uncertainty Perspective
- On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
- On the Separability of Information in Diffusion Models
- On the Sharp Input-Output Analysis of Nonlinear Systems under Adversarial Attacks
- On the Theoretical Limitations of Embedding-based Link Prediction
- On the Theory of Continual Learning with Gradient Descent for Neural Networks
- On The Variability Of Concept Activation Vectors
- On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
- On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
- OOVDet: Low-Density Prior Learning for Zero-Shot Out-of-Vocabulary Object Detection
- Op-CAD: Benchmarking and Investigating Operation-oriented CAD Generation
- OpenDeception: Learning Deception and Trust in Human–AI Interaction via Multi-Agent Simulation
- OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing
- OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
- OpenIKLR: Bridging the Reasoning Gap in Open-World Scenarios via Iterative Premise Completion
- OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
- Open Materials Generation with Inference-Time Reinforcement Learning
- Open-o3-Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence
- OpenSage: Self-programming Agent Generation Engine
- Open-Text Aerial Detection: A Unified Framework For Aerial Visual Grounding And Detection
- OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data
- Operationalizing the Superficial Alignment Hypothesis via Task Complexity
- Operator Splitting with Hamilton-Jacobi-based Proximals
- Ophiuchus: Incentivizing Tool-augmented ''Think with Images'' for Joint Medical Segmentation, Understanding and Reasoning
- OPIC: Enhancing Language Model Merging via Optimizing In-Context Capability
- Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining
- OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling
- OptiFluence: Principled Design of Privacy Canaries
- Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schrödinger Bridges
- Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints
- Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions
- Optimal Bayesian Stopping for Efficient Inference of Consistent LLM Answers
- Optimal Classical and Quantum Algorithms for Gradient Testing and Estimation by Comparisons
- Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
- Optimal Decision-Making Based on Prediction Sets
- Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
- Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
- Optimal Estimation of Continuous Treatment Effects with Kernel Ridge Regression
- Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints
- Optimality of FSQ tokens for continuous diffusion for categorical data with application to text-to-speech
- Optimal Learning from Label Proportions with General Loss Functions
- Optimal Pricing for Data-Augmented AutoML Marketplaces
- Optimal Quantum Speedups for Repeatedly Nested Expectation Estimation
- Optimal Rates for Feasible Payoff Set Estimation in Games
- Optimal Regret for Policy Optimization in Contextual Bandits
- Optimal Regularization for Performative Learning
- Optimal Self-Consistency for Efficient Reasoning with Large Language Models
- Optimal Splitting of Language Models from Mixtures to Specialized Domains
- Optimal Statistical Guarantees for Diffusion Models on Low-Dimensional, Multi-Modal Data
- Optimal Stopping in Latent Diffusion Models
- Optimal structure learning and conditional independence testing
- Optimal Top-$k$ Identification from Pairwise Comparisons
- Optimal Transport for Reward Modeling from Noisy Feedback
- Optimal Transport Group Counterfactual Explanations
- Optimal Transport–Guided Stochastic Control for Graph Combinatorial Optimization
- Optimal Transport under Group Fairness Constraints
- Optimal Transport with Symmetry Groups
- Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
- Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov–Arnold Networks
- Optimized Deferral for Imbalanced Settings
- Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward
- Optimizing Diversity and Quality through Base-Aligned Model Collaboration
- Optimizing Few-Step Generation with Adaptive Matching Distillation
- Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification
- Optimizing KV Cache Eviction from an Output Perturbation Perspective
- Optimizing Language Models for Crosslingual Knowledge Consistency
- Optimizing Network Simulation: Enhancing Performance Prediction Accuracy via Neural Architecture Search
- Optimizing Rank for High-Fidelity Implicit Neural Representations
- Optimizing Visual Generative Models via Distribution-wise Rewards
- OPTION: Optimal Transport–Guided Flow Matching for Incomplete and Unaligned Multi-View Clustering
- OptMaster: A DAG-Based Framework for Formulation and Heuristic Discovery in Optimization
- Opt-Miner: Empowering Information-Seeking Agent with Tree-Guided Data Synthesis for Optimization Modeling
- OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
- Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
- OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration
- ORBIT: A Prognostic World Model for Ocular Reasoning Based on Imagined Trajectories
- Orchestrating Spatial Semantics via a Zone-Graph Paradigm for Intricate Indoor Scene Generation
- OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing
- Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
- Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution
- Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization
- Origo: Physically Interpretable Multi-Physics PDE Pre-training through Neural Operator Splitting
- Orthogonal Concept Erasure for Diffusion Models
- Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models
- Orthogonal Model Merging
- OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization
- OSCS: Online Selection with Provable FAR Control for LLM Safety
- OServe: Accelerating LLM Serving via Spatial-Temporal Workload Orchestration
- OSF: On Pre-training and Scaling of Sleep Foundation Models
- OSM+: Billion-Level Open Street Map Dataset for City-wide Experiments
- OSNIP: Breaking the Privacy-Utility-Efficiency Trilemma in LLM Inference via Obfuscated Semantic Null Space
- OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents
- Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
- Outcome-Based Rewards Do Not Guarantee Faithful and Verifiable Reasoning
- Out-of-Distribution Evaluation of Rule-Based and Strategic Reasoning in Chess Transformers
- Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All
- Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization
- Overclocking Electrostatic Generative Models
- Overcoming PINNs Failure Modes In High Dimension With Low-Rank Fourier Sum
- Overcoming the Incentive Collapse Paradox
- Overcoming the Modality Gap in Context-Aided Forecasting
- Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets
- OvisOCR: End-to-End Document Parsing via Aligning Specialized Perception with General Reasoning
- OVLR: Efficient, Scalable, and Robust Training via Output-Level Variance-Reduced Likelihood Ratio
- OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning
- PAC-Bayesian Reinforcement Learning Trains Generalizable Policies
- PACEAttention: Principled and Adaptive Feature Compression-Expansion Grounded in the Geometry of $\text{MCR}^2$
- PACE: Parameter Change for Unsupervised Environment Design
- PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
- PACE: Proactive Agent-Level Admission Control for Efficient Agentic Batch Inference
- PACER: Acyclic Causal Discovery from Large-scale Interventional Data
- PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
- PADA-Coder: Improving Plan-Following Code Generation via Perturbation-Verified Attention Distillation and Dynamic Alignment
- PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning
- PADS-TAL: Padding-Annealed Diffusion Sampling in Text-Aware Latent Space for Robust and Diverse Text-to-Music Generation
- Pair2Scene: Learning Local Object Relations for Procedural Scene Generation
- PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning
- Panini: Continual Learning in Token Space via Structured Memory
- PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion
- PaperBanana: Automating Academic Illustration for AI Scientists
- ParalESN: Enabling parallel information processing in Reservoir Computing
- Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
- Parallel Stochastic Gradient-Based Planning for World Models
- Parameter Decorrelation via Transition-Variance Alignment for Multivariate Time-series Forecasting
- Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory
- Parameter Manifold Purification
- Parameter-Masked Decoupled Optimization for Cross-Domain Class-Incremental Learning
- Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing for Dense Predictions
- Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
- Parametrized Power-Iteration Clustering for Directed Graphs
- ParamMem: Augmenting Language Agents with Parametric Reflective Memory
- ParaTool: Shifting Tool Representations from Context to Parameters
- Pareto-Guided Optimal Transport for Multi-Reward Alignment
- ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
- ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMs
- Parsimonious Learning-Augmented Online Metric Matching
- PartCo: Part-Level Correspondence Priors Enhance Category Discovery
- Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation
- Partial Identification under High-Dimensional Potential Outcomes and Confounders via Optimal Transport
- Partial Ring Scan: Revisiting Scan Order in Vision State Space Models
- Particle Flow for Learning from Label Proportions
- Particle-Guided Diffusion Models for Partial Differential Equations
- Particles Don’t Care About Z: Towards Scaling Entropy Estimation of Unnormalized Densities
- Partitioning for Intrinsic Model Inversion Resistance in Collaborative Inference
- PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks
- PASO: Step Parallel Stochastic Optimization
- PATCHCODE: Discrete Latent Predictive Learning for EEG Foundation Model
- Path-conditioned training: a principled way to rescale ReLU neural networks
- Path-Coupled Bellman Flows for Distributional Reinforcement Learning
- Path-Decoupled Hyperbolic Flow Matching for Few-Shot Adaptation
- Path-dependent Discrete Amortized Inference
- PathwayLLM: Explainable Clinical Trajectory Modeling with Structured Pathways for Sepsis Prediction
- PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs
- PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering
- Patterning: The Dual of Interpretability
- PatternKV: Flattening KV Representation Expands Quantization Headroom
- PAWS: Preference Learning with Advantage-Weighted Segments
- PCA of Probability Measures: Sparse and Dense Sampling Regimes
- PCGS: Deblurring 3D Gaussian Splatting with Patch Comparison
- PCRNet: Phase-aware Complex Refinement Network for EEG-based Auditory Attention Decoding
- PDAgent: An LLM-Driven Autonomous Agent Framework Towards *In Silico* Protein Design via Directed Mutation
- PDFBench: A Benchmark for De Novo Protein Design from Function
- PEARL: Differentially Private and Entropy-Aware Regulated Language Generation
- Peer-Preservation in Frontier Models
- PepCompass: Navigating Peptide Embedding Spaces Using Riemannian Geometry
- PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception
- PerceptOS: Semantic-Aware Kernel Optimization for OS-Intensive Workloads via Hardware-Software Alignment
- Perceptrons and Localization of Attention’s Mean-Field Landscape
- Perceptual Flow Network for Visually Grounded Reasoning
- Per-example Gradients: a New Frontier for Understanding and Improving Optimizers
- Performative Learning Theory
- Performative Policy Gradient: Optimality in Performative Reinforcement Learning
- Periodic Bayesian Flow Networks with Additive Accuracy
- PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?
- Persistent Backdoor Attacks in Class-Incremental Learning via Structural Invariant Anchoring
- Persistent Semantic Entities in Tool-Augmented LLM Systems
- Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
- Personalized Additive Modeling for Multi-level Federated Learning
- Personalized Image Generation via Human-in-the-loop Bayesian Optimization
- Personalized Policy Learning through Discrete Experimentation
- Persona-Pruner: Sculpting Lightweight Models for Role-Playing
- Persuasive Privacy
- PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling
- PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
- Pessimistic Verification for Open-Ended Math Questions
- PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
- PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
- PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection
- PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-Scale physics simulations
- PGS: Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback
- PGT: Procedurally Generated Tasks for improving fine-grained understanding in MLLMs
- PHALAR: Phasors for Learned Musical Audio Representations
- PhaseAlign: Complex Phase Alignment for Stable Open-Vocabulary Semantic Segmentation
- Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
- PhaseCoder: Microphone Geometry-Agnostic Spatial Audio Understanding for Multimodal LLMs
- Phase-Type Variational Autoencoders for Heavy-Tailed Data
- PhenoBrain: Phenotype-Conditioned Long-Range Communication for Multi-Modal Brain Network Analysis
- Philosophy Meets Machine Learning: What Counts as Trustworthy?
- PhoStream: Benchmarking Real-World Streaming for Omnimodal Assistants in Mobile Scenarios
- PhotoAgent: Exploratory Visual Aesthetic Planning with Large Vision Models
- Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Spectral Super-Resolution for Snapshot Compressive Imaging
- PhyScene3D: Physically Consistent 3D Interactive Tabletop Scene Generation
- PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World
- PhysHanDI: Physics-Based Reconstruction of Hand-Deformable Object Interactions
- Physically-Guided Data-Space Rectified Flow for Precipitation Nowcasting
- Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions
- Physics-Guided Motion Loss for Video Generation Model
- Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
- Physics-informed coarsening for multigrid graph neural networks surrogates
- Physics-informed diffusion models in spectral space
- Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
- Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation
- Physics-Informed Residual Flows
- Physics-Informed Self-Supervised Learning on Efficient Electron-Density Images for Organic Material Property Prediction
- Physiology as Language: Translating Nocturnal Breathing to EEG
- Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning
- Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training
- PICACO: Pluralistic In-Context Value Alignment via Total Correlation Optimization
- PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
- PINNfluence: Interpreting PINNs through Influence Functions
- PinTok: Tokenizers Deserve Dedicated Pinned CPU-Compute and Memory
- PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
- PISA: Privacy-Preserving Split Adaptation with Model IP Protection
- PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards
- Pix2Key: Controllable Open-Vocabulary Retrieval with Semantic Decomposition and Self-Supervised Visual Dictionary Learning
- PixCLIP: Towards Fine-grained Vision-Language Understanding via Any-granularity Pixel-Text Alignment
- PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Design
- Plain Transformers are Surprisingly Powerful Link Predictors
- Planar Symmetric Pattern Generation
- Plan, Decouple, Assimilate: Physics-Aware Object Insertion in Remote Sensing Imagery
- Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models
- Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation
- Planning in The Era of Language Models (LM4Plan)
- PLANTAIN: Plan-Answer Interleaved Reasoning
- Plan Then Action: High-Level Planning Guidance Reinforcement Learning for LLM Reasoning
- PLASH: Provably Linear-Time Attention with Selective Higher-Order Feature Sketching
- Plasticity Activation via Polar Operator: A Plug-in Method for Balancing Stability and Plasticity
- PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning
- Platonic Transformers: A Solid Choice For Equivariance
- PLoRA: Efficient Concurrent LoRA Training for Large Language Models
- PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization
- PLSemanticsBench: A Formal Semantics Reasoning Benchmark for Code
- Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
- Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
- Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction
- Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation
- Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers
- PlugGuard: A Streaming Safeguard for Large Models via Latent Dynamics-Guided Risk Detection
- PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
- Pluralistic Leaderboards
- PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
- PMSPO: Progressive Matching and Semantic-Aware Policy Optimization for Camouflaged Object Detection
- PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
- PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
- POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation
- PointCHR: Point Cloud Analysis via Curvature-Aware Hyperbolic Rectification
- PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
- Poison with Style: A Practical Poisoning Attack on Code Large Language Models
- PolarDepth: Monocular Transparent Object Depth from Polar-Physics Priors
- Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
- POLCA: Stochastic Generative Optimization with LLM
- POLIA: Policy Optimization with Visual-Object-Level Intrinsic Advantage for Multimodal Reasoning
- Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning
- PolicyGuard: Towards Test-time and Step-level Backdoor Defense for Reinforcement Learning Agent
- Policy Search via Bayesian Optimization with Temporal Difference Gaussian Processes
- Polishing-Only Policies in Peer Reviews are Currently Not Enforceable
- PolyFlow: Safe and Efficient Polytope-Constrained Flow Matching with Constraint Embedding and Projection-free Update
- Polyphonia: Training-Free Context-Aware Music Editing with Acoustic-Informed Attention Calibration
- PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding
- PoMtVRS: Preference-Optimized Multi-Task Vehicle Routing Solver with Preference Gating
- PonderLM-2: Pretraining LLM with Latent Thoughts in Continuous Space
- Population-Aware Imitation Learning in Mean-field Games with Common Noise
- Population-Free Pareto Tracking for Sample-Efficient Multi-Policy MORL
- PortraitRL: Reinforcement Learning for Personalized Portrait Pose Transfer with Multi-Objective Reward Modeling
- Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
- Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
- Position: Accountable Deployment of Agentic AI Demands Layered, System-Level Interpretability
- Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost
- Position: Adversarial ML for LLMs Is Not Making Any Progress
- Position: Age Estimation Models Do Not Process Biometric Data
- Position: Agent Evaluation Should Be Agentified for Openness, Standardization, and Reproducibility
- Position: Agentic AI Is a Foreseeable Pathway to AGI
- Position: Agentic AI systems should be making Bayes-consistent decisions
- Position: Agentic Safety is an Epistemic Property, Not a Behavioral One
- Position: Agentic Systems Should be General
- Position: Agent Security Needs Redefinition through a Holistic Framework
- Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
- Position: AGI Requires a Coordination Layer on Top of Pattern Repositories
- Position: `AI Alignment' Encompasses Competing Technical Priorities
- Position: AI Capabilities Are Not Increasing Exponentially
- Position: AI Evaluation Should Work With Humans
- Position: AI Evaluations Should be Grounded on a Theory of Capability
- Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components
- Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws
- Position: AI Leaderboards Are Underserving the Global South: A Case Study from India
- Position: AI Lock-In Is in Progress, and We Must Be Prepared
- Position: AI/ML Deepfake Research is Misaligned with AI Generated Non-Consensual Intimate Imagery (AIG-NCII)
- Position: AI Must Become Planet-Centered, Not Human-Centered
- Position: AI Researchers Must Lead Arms Control to Mitigate Military AI Risks
- Position: AI Should Facilitate Democratic Deliberation at Scale
- Position: AI Usage Policies Should Be Aligned with International Human Rights Law
- Position: AI Welfare Is Bullshit
- Positional Encoding for Spiking Transformers
- Position: Anthropomorphic Misalignment Research Needs Stronger Evidence
- Position: Artificial Intelligence Needs Meta Intelligence - the Case for Metacognitive AI
- Position: Assistive Agents Need Accessibility Alignment
- Position: Assistive AI requires Personalized Specialists, not Generalists
- Position: Behavioral Systems Require Behavioral Tests
- Position: Benchmarks Do Not Measure Deployment Readiness in Clinical AI
- Position: Benchmarks for Vision–Language Models in Urban Perception Should Be Reliability-Aware and Negotiated
- Position: Beyond Prediction: Toward Verifiable Physiological Waveform Reasoning with Foundation Models and Agentic LLMs
- Position: Beyond Reasoning Zombies — AI Reasoning Requires Process Validity
- Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
- Position: *Beyond Text* The Text-Centric Bias in Foundation Models Must Be Revisited for a Speech-First Future
- Position: Breaking the Dual Curse of Multilingual AI Requires Socio-Technical Guardrails, Not Post-Hoc Alignment
- Position: Bridge Human Interpretation and Machine Representation With Explicit Specification For Qualitative Data Analysis In LLM Era
- Position: Bridge the Gaps between AI Development and Regulation
- Position: Carbon Footprint Reporting Should Be Routine in Machine Learning Research
- Position: Causality is Key for Interpretability Claims to Generalise
- Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration
- Position: Child Safety Necessitates New Approaches to AI Safety
- Position: Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility
- Position: Collaborative Agentic AI Needs Interoperability Across Ecosystems
- Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions
- Position: Comprehensive AI governance requires addressing non-model capability gains
- Position: Creating High-Fidelity Synthetic Training Data Should Employ Multi-level Optimization
- Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting
- Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models
- Position: Current XAI Methods Cannot Satisfy Financial AI Explainability Requirements
- Position: Deciphering the Functions of DNAs, RNAs, and Proteins Should Consider Multi-Modal Large Language Models
- Position: Deployed Reinforcement Learning should be Continual
- Position: Digital Agents Require Unified Agent-Native Environments
- Position: Don't Just "Fix it in Post'': A Science of AI Must Study Learning Dynamics
- Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation
- Position: Embodied AI Requires a Privacy-Utility Tradeoff
- Position: Enabling Fair Revenue Sharing for Data Providers in GenAI Systems
- Position: Epistemic uncertainty estimation methods are fundamentally incomplete
- Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences
- Position: Evaluating LLMs in Finance Requires Explicit Bias Consideration
- Position: Evaluation of ECG Representations Must Be Fixed
- Position: Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
- Position: Every Ground Truth is a Human Construction, not an Objective Truth
- Position: Evidence and Implications of Texture Bias in Deep Neural Networks
- Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
- Position: Explanation Stability Is a Property of the Model–Method Pair, Not the Model
- Position: Express Your Doubts — Probabilistic World Modeling Should not be Based on Token *logprobs*
- Position: Fairness Failure in Generative Models is an Evaluation Problem
- Position: Federated Learning is a Lens towards a Democratized Future for the Scaling Law Era
- Position: From Crowdsourcing to Crowd-LLM-Sourcing and LLM-Sourcing
- Position: Generative Distributional Integrity against Backdoor Attacks
- Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
- Position: Generative Models Erode Temporal Learning Through Market Selection
- Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods
- Position: Good Embodied Reward Models Need Bad Behavior Data
- Position: Graph Condensation Needs a Reset—Move Beyond Full-dataset Training and Model-Dependence
- Position: Hallucinations Undermine Trust; Metacognition is a Way Forward
- Position: Hippocampal Explicit Memory Is a Cornerstone to Human-Level AI
- Position: Human-Centric Vision Requires Topological Generalization Beyond Fixed Skeletal Topologies
- Position: ICML Should Treat Hosted LLM APIs as Versioned Dependencies and Require Drift-Audit Artifacts
- Position: Ideas Should be the Center of Machine Learning Research
- Position: If open source is to win, it must go public
- Position: Improved Documentation is Necessary for Benchmarking AI Systems in Geometry
- Position: In Defense of Information Leakage in Concept-based Models
- Position: Interestingness is an Inductive Heuristic for Future Compression Progress
- Position: Interpretability Can Be Actionable
- Position: Interpretability in Deep Time Series Models Demands Semantic Alignment
- Position: Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services
- Position: Irresponsible AI: big tech’s influence on AI research and associated impacts
- Position Is All You Need: A Free Lunch Token Compression Strategy for MLLM-based Referring Expression Segmentation
- Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer
- Position: It’s Time to Optimize for Self-Consistency
- Position: Knowing Isn’t Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight
- Position: Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences
- Position: Let’s Build a Trustworthy Model Context Protocol!
- Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
- Position: LLM Agents Are the Antidote to Walled Gardens
- Position: LLM-Based Social Simulations Require a Boundary
- Position: LLM Benchmark Datasets should be Contamination-Resistant
- Position: LLM for Physics Research Requires Domain-Specialized Training and Tooling
- Position: LLM-Safety Evaluations Lack Robustness
- Position: LLMs can't jump
- Position: LLM Serving Needs Mathematical Optimization and Algorithmic Foundations, Not Just Heuristics
- Position: LLMs Should Incorporate Explicit Mechanisms for Human Empathy
- Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives
- Position: Machine Learning Research Should Be Guided by Explicit, Pluralistic Models of Human Purpose
- Position: Make Planning Research Rigorous Again!
- Position: Measuring Human Preferences in RLHF is a Social Science Problem
- Position: Mechanisms for Aggregated Individual Reporting Should be Established for Post-Deployment Evaluation
- Position: Medical AI Neglects Real Treatment Outcomes
- Position: Metaphysical Concepts in AI Should Be Judged by Their Consequences
- Position: Model identity in machine learning is a convention, not a property
- Position: Modular Memory is the Key to Continual Learning Agents
- Position: Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World
- Position: Multi-Agent Explainability Needs Contracts Before Methods
- Position: Multi-Agent Systems Should Prioritize Concurrency Control
- Position: Multiple Definitions & Unrealistic Assumptions of Model Collapse Distract from Real World Threats
- Position: Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning
- Position: Natural Language Should Not Fully Replace Formal Languages
- Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
- Position: Neural Approximation Is Rarely Justified for Hard Combinatorial Problems
- Position: No Retroactive Cure for Infringement during Training
- Position: Peer Review in ML/AI Conferences Should Separate Publication from Presentation and Offer Non-Anonymous Review Tracks
- Position: Peer Review Should Be Calibrated via LLM Scoring
- Position: Predicting AI’s Impact on Labor Is a Core Machine Learning Problem
- Position: Predictive Uncertainty Is Not Enough -- Joint Distribution for Full Uncertainty Representation
- Position: Preparing for AI Systems That Deceive Developers
- Position: Preregister Experiments with AI Agents
- Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
- Position: Privacy Is a Claim, Not a Property of Synthetic Data
- Position: Profiling Game Worlds by Transition Complexity
- Position: Prompting Intent Should Be Audited in LLM-Assisted Peer Review
- Position: Prompts for Public-Sector LLMs Should Be Governed as Commons
- Position: Quantum Deep Learning Still Needs a Quantum Leap
- Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential
- Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling
- Position: Reasoning After Perception Means Reasoning Without Vision
- Position: Reframing Hallucination: Latent Space Geodesics as a Pathway for Generative Discovery
- Position: Regulating Algorithms Is Not Enough. A Study of Content Discovery in Online Platforms
- Position: Reliable AI Needs to Externalize Implicit Knowledge: A Human–AI Collaboration Perspective
- Position: Responsible AI for AI companions must actively combat violence toward intimate partners
- Position: Responsible Practices and Model Performance are Not Competing Goals
- Position: Retire the "Positive Backdoor" Label—Secret Alignment Requires Strict and Systematic Evaluation
- Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
- Position: RL Should Be Used to Adjust Foundation Models, NOT Abused
- Position: Robust AI Personalization Will Require a Human Context Protocol
- Position: Safe AI Should be Resistant and Resilient in an Evolving World
- Position: Safe Models Do Not Guarantee Safe Societies: The Case for Sociopolitical Risk
- Position: Safety Must Precede the Deployment of Open-Ended AI Agents
- Position: Scale is a False Promise for Endangered Languages
- Position: Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain
- Position: Significant impact of numerical precision in scientific machine learning
- Position: Solipsistic superintelligence is unlikely to be cooperative
- Position: Spatial Fairness: Foundations, Pitfalls, and a Path Forward
- Position: State-of-the-Art Claims Require State-of-the-Art Evidence
- Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
- Position: Stop Automating Peer Review Without Rigorous Evaluation
- Position: Stop Chasing the C-index when Evaluating Survival Analysis Models
- Position: Stop evaluating AI with human tests, develop principled, AI-specific tests instead
- Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI
- Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development
- Position: Stop Using Culturally Biased Human Cognitive Benchmarks to Evaluate LLMs
- Position: Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives
- Position: Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks
- Position: Temporal Measurement Interval Determines Computational and Model Complexity in Single-Cell Perturbation Analysis
- Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning
- Position: The Age of AI Agents Demands A New Scientific Paradigm To Sustain Trustworthy Science
- Position: The AI Imperative: Scaling High-Quality Peer Review in Machine Learning
- Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit
- Position: The Case for Theory-Level Autoformalization
- Position: The Data Provenance–Parametric Divide in Large Language Models
- Position: The Inevitable Transition to Machine Learning in Quantum Chemistry
- Position: The Machine Learning Community Must Treat Compute Inequality as a First-Class Research Problem
- Position: The Open Benchmark Paradox Must Be Resolved through Sovereign Medical Evaluation
- Position: The Privacy-Auditability Paradox in Federated Learning: Why We Need Controllable Secure Aggregation
- Position: There are futures that benchmark-driven AI cannot see
- Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
- Position: The Systemic Lack of Agency in Visual Reasoning
- Position: The Term “Machine Unlearning” Is Overused in LLMs
- Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
- Position: The Turing-Completeness of Real-World Autoregressive Transformers Relies Heavily on Context Management
- Position: Time-Series Foundation Models Require Explicit Domain-Level Benchmarks
- Position: Time to Close The Validation Gap in LLM Social Simulations
- Position: To Defend Against Cyber Attacks, We Must Teach AI Agents to Hack
- Position: Token Taxes Can Mitigate AI's Economic Risks
- Position: Topological Machine Learning Cannot Progress without Experimental Standards
- Position: Towards Responsible Evaluation for Text-to-Speech
- Position: Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
- Position: Uncertainty is a Strategic Signal in Human–AI Decision Making
- Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
- Position: Universal Aesthetic Alignment Narrows Artistic Expression
- Position: Unlabeled ≠ No Human Supervision in Visual Learning
- Position: Unplugging a Seemingly Sentient Machine Is the Rational Choice — A Metaphysical Perspective
- Position: Use Sparse Autoencoders to Discover Unknowns
- Position: Vector Prompt Interfaces Should Be Exposed to Enable Customization of Large Language Models
- Position: Verifiable Data Minimization is a Prerequisite for Responsible, Privacy-Preserving Industrial Vision
- Position: Video LLMs Must Not Ignore the Pixel Dynamics in Plain Sight
- Position: Virtual Cells Need Context, Not Just Scale
- Position: Vision encoders should be image size agnostic and task driven
- Position: VLM Causal Reasoning Benchmarks Should Probe Temporal Understanding, Not Presume It
- Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System
- Position: Web Agents Should Use Typed Actions Instead of Click-Based Browsing
- Position: Weight Space Should Be a First-Class Generative AI Modality
- Position: We Need AI Efficiency Incentives for Accessibility and Sustainability
- Position: We Need A Unified Definition of Hallucination (It’s The World Model, Stupid!)
- Position: We Need Large Language Models Optimized For Our Well-Being
- Position: We Need Practical AI Alignment Methods that Mirror Human Reasoning
- Position: We need to re-think the concept of “real” images.
- Position: When AI Decides Who Gets an Organ: Multi-Agentic AI Systems in Transplant Medicine Risk Amplifying Disparities Without Targeted Explainability and Deployment Strategies
- Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling
- Position: World Models as an Intermediary between Agents and the Real World
- Position: Your VLM May Not Be Thinking with Interleaved Images
- Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered
- Positive Distribution Shift as a Framework for Understanding Tractable Learning
- Positive-Unlabeled Learning with Extreme Scarcity of Labeled Positives
- Positive–Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
- Possibilistic Predictive Uncertainty for Deep Learning
- PosterAgent: Agentic Poster Generation via Stage-Aware Reinforcement Learning
- Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning
- Posterior Concentration of Physics-Informed Neural Networks for Elliptic PDEs
- Posterior Mismatch Matters: Adversarial Training for Long-Tailed Robustness
- Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
- Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing
- PostTrainBench: Can LLM Agents Automate LLM Post-Training?
- Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
- Post-Training with Policy Gradients: Optimality and the Base Model Barrier
- Power-Boosted Granger-Causal Discovery for Large Heterogeneous Panel Data
- Power-Calibrated LLM Watermarking: A Statistical Framework
- PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
- Powerful and Theoretically Guaranteed Independence Testing on Heterogeneous Federated Clients
- PPDL: LLM-Based Flows as Probabilistic Programs
- PPI Candidate Ranking: Large-Scale Evaluation of a Domain Knowledge–Guided Pipeline
- PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks
- PRAC: Principal-Random Subspace for LLM Activation Compression and Memory-Efficient Training
- Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
- Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test
- Practical Mechanism for Fault-Tolerant Spiking Neural Networks via Simple Input Control Based on Learnable Fragmentation
- PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts
- Precision-Induced Miscalibration: Understanding and Correcting Confidence Distortion in Quantized Neural Networks
- Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences
- Preconditioning Neural Tangent Kernel for Adaptive Optimization
- Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication
- Predicting Dynamic Stability Landscapes in Synchronization Networks
- Predicting evolutionary rate as a pretraining task improves genome language model representations
- Predicting Future KV Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction
- Predicting Large Model Test Losses with a Noisy Quadratic System
- Predicting the Emergence of Induction Heads in Language Model Pretraining
- Predicting the Order of Upcoming Tokens Improves Language Modeling
- Predicting What Matters: Robust Generalist Robot Policy Learning via Future Semantic Mask
- Prediction-Powered Adaptive Inference with Pretrained AI Models for Contextual Bandits
- Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts
- Predictive Prefetching for Retrieval-Augmented Generation
- Predictive variational inference: Learn the predictively optimal posterior distribution
- Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision
- Preference-Calibrated Optimization with Score-Level Distribution Alignment for Text-to-Image Diffusion Model Unlearning
- Preference-Enhanced Reinforcement Learning for Pluralistic Image Inpainting
- Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies
- Preference-Modulated Structural Attention for Multi-Objective Combinatorial Optimization
- Prefix cache aware data reordering for LLM augmented database analytics
- Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
- Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs
- Preserving Plasticity in Continual Learning via Dynamical Isometry
- Pressure Reveals Character: Behavioural Alignment Evaluation at Depth
- Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
- PretrainZero: Reinforcement Active Pretraining
- Primal-Spectral Generative Modeling: Fast Analytical Generation via Pseudoinverse Lévy Inversion
- PRIM：Cooperative Dynamic Token Compression for Efficient Large Multimodal Models
- Principled RL for Flow Matching Emerges From the Chunk-level Policy Optimization
- Principled SVD-based Delta Compression via Quantization Error Minimization
- Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
- Principled Zero-shot Ranking Agents with Tournament Graphs
- Principle-Evolvable Scientific Discovery via Uncertainty Minimization
- Prior Diffusiveness and Regret in the Linear-Gaussian Bandit
- Prioritized Model Experience Replay
- Prioritize the Process, Not Just the Outcome: Rewarding Latent Thought Trajectories Improves Reasoning in Looped Language Models
- Priority-Aware Shapley Value
- PRISM: Demystifying Retention and Interaction in Mid-Training
- PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
- PRISM: Gauge-Invariant Tangent-Space Differentially Private LoRA
- Prism-MoE: Efficient Dense-to-MoE Conversion for Visual Autoregressive Generation
- PRISM: Perception Reasoning Interleaved for Sequential Decision Making.
- PRISM: Sequence Modeling as Parallel Residual Iteration
- Prism: Spectral-Aware Block-Sparse Attention
- PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
- PRISM: Training-Free Video Anomaly Detection via Intrinsic Statistical Modeling
- PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training
- Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
- Privacy-Aware Data Integration for Enhanced Quantile Inference under Heterogeneity
- Privacy-Aware Video Anomaly Detection: Guided Orthogonal Projection and a Comprehensive Evaluation Framework
- Privacy Risks of Agentic Inferential Capabilities in Data Linkage Attacks
- Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch
- Private and Stable Test-time Adaptation with Differential Privacy
- Private Learning with Public Feature Conditioning
- Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
- PrivCode++ : Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees
- PrivGate: Steering Contextual Integrity in LLMs via Latent Space Geometry
- Privileged Information Distillation for Language Models
- PRM-PBE: Process Reward Model for Reinforcement Learning in Programming-by-Example
- ProAct: A Benchmark and Multimodal Framework for Structure-Aware Proactive Response
- Proactive Defense Benchmark against Deepfake Generation
- ProactiveLLM: Learning Active Interaction for Streaming Large Language Models
- Proact-VL: A Proactive VideoLLM for Real-Time AI Companions
- Probabilistically-routed Bayesian Additive Spanning Trees for Learning on Constrained Domains
- Probabilistic Bisection Algorithm Provably Achieves Exponential Convergence
- Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks
- Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning
- Probabilistic Retrofitting of Learned Simulators
- Probabilistic Robustness Certificates against Adversarial Attacks
- Probabilistic Salient Object Ranking
- Probability-Entropy Calibration: An Elastic Indicator for Adaptive Fine-tuning
- Probability of Matching for Batch Multi-Objective Bayesian Optimization
- Probably Approximately Correct Labels
- ProbeLLM: Automating Principled Diagnosis of LLM Failures
- Probing Cross-modal Information Hubs in Audio-Visual LLMs
- Probing How Scalable Table Data Enhances General Long-Context Reasoning
- Probing Newtonian Mechanics in Video Generative Models with Real Physical Systems
- Probing RLVR Training Instability through the Lens of Objective-Level Hacking
- Probing the Geometry of Diffusion Models with the String Method
- Probing the Inductive Bias of Neural Networks through Learning Random Cellular Automata
- Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction
- Problem Distributions as Tasks: Repurposing Meta Learning for Generative Combinatorial Optimization towards Multi-task Pretrain and Adaptation
- Procedural Generation Of Algorithm Discovery Tasks in Machine Learning
- Procedural Pretraining: Warming Up Language Models with Abstract Data
- Process Reward Agents for Steering Knowledge-Intensive Reasoning
- ProcMEM: Learning Reusable Procedural Memory from Experience via Non-Parametric PPO for LLM Agents
- ProConMV: Provenance-Enabled Conceptual Framework for Interpretable Multi-View Diabetic Retinopathy Diagnosis
- ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
- Profiling the Irrational Agent: Cognitive Modeling of LLM Behaviors in Sequential Jailbreaks
- Progressive Cramming: Reliable Token Compression and What It Reveals
- Progressive Graph Structure Adjustment for Homophily Shift Adaptation
- Projected Gradient Ascent for Efficient Reward-Guided Updates with One-Step Generative Models
- Projection-Free Algorithms for Minimax Problems
- ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
- ProMeCD: Unifying Long-Tailed and Noisy Label Learning via White-Box Control
- ProMiSE: Protein Multi-state Structure Evaluation Benchmark in Biological Contexts
- PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
- Prompt Injection as Role Confusion
- Prompt Optimization with Minimal Unlabeled Input via Meta-Reasoning
- PromptPilot: Game-Theoretic Multi-Agent Prompt Optimization for Segment Anything
- Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers
- PromptRL: Prompt Matters in RL for Flow-Based Image Generation
- Prompt Tuning for CLIP on the Pretrained Manifold
- ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling
- ProphetKV: User-Query-Driven Selective Recomputation for Efficient KV Cache Reuse in Retrieval-Augmented Generation
- Propose, Solve, Verify: Self-Play Through Formal Verification
- ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation
- ProSAR: Prototype-Guided Semantic Augmentation and Refinement for Time Series Contrastive Learning
- ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
- Protein Autoregressive Modeling via Multiscale Structure Generation
- Protein Circuit Tracing via Cross-layer Transcoders
- Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents
- Protein Fold Classification at Scale: Benchmarking and Pretraining
- Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
- Proteo-R1: Thinking Foundation Models for De Novo Protein Binder Design
- Proteus: Lookup-Free Trellis-Coded Quantization by Lattice-Breaking Compute Codes for 2-Bit LLMs
- ProtoKV: Streaming Video Understanding under Delayed Evidence with Summary-State Memory
- Prototype-Based Test-Time Adaptation of Vision-Language Models
- Prototype-Grounded Concept Models for Verifiable Concept Alignment
- Prototype-guided Bilateral Alignment Multimodal Federated Learning
- Prototype Transformer: Towards Language Model Architectures Interpretable by Design
- ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling
- Provable Accuracy Collapse of Embedding-Based Representations under Dimensionality Mismatch
- Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently
- Provable Bounds for the Learnability of Sample-Compressible Families from Noisy Samples
- Provable Sample Efficiency of Curriculum Post-Training for Transformer Reasoning
- Provable Training Data Identification for Large Language Models
- Provably Adaptive Linear Approximation for the Shapley Value and Beyond
- Provably Convergent Actor-Critic in Risk-averse MARL
- Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
- Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
- Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning
- Provably Label-Efficient Conformal Prediction
- Provably Learning Attention with Queries
- Provably Protecting Fine-Tuned LLMs from Training Data Extraction
- Provably Valid Uncertainty Quantification for Deep Computed Tomography
- Proximal-Based Generative Modeling for Bayesian Inverse Problems
- Proximal Decoding: Provably Reducing Copyright Risk for Any Language Model
- Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis–Hastings with Approximate Operators
- Proximal Splitting Methods for Hybrid Differentiable Models
- Proxy Compression for Language Modeling
- PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
- Pruning at Initialisation through the lens of Graphon Limit: Convergence, Expressivity, and Generalisation
- PSBench: Editing Image via GUI Agents in Photoshop
- Pseudo-Mallows for Efficient Probabilistic Preference Learning
- PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making
- PSMix: Robust Point Cloud Recognition through Spectral Domain Mixing
- PS-PPO : Prefix-Sampling PPO for Critic-Free RLHF
- PsumQuant: In-line Post-training Partial Sum Quantizer for Energy Efficient NPU Inference
- pTNAS: Progressive Neural Architecture Search for Tabular Data
- Pull Requests as a Training Signal for Repo-Level Code Editing
- PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
- Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
- Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
- Push, Pop, Parallelize: Stack-Augmented Linear Attention via the Delta Rule
- PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
- PVDepth: Panoramic Video Depth Estimation via Geometry-Aware Spatiotemporal Adaptation
- PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
- PyVision-RL: Forging Open Agentic Vision Models via RL
- Q-CLIP: Unleashing the Power of Vision-Language Models for Video Quality Assessment through Unified Cross-Modal Adaptation
- Q-Delta: Beyond Key–Value Associative State Evolution
- Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution
- QEDBench: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
- Q-Flow: Stable and Expressive Reinforcement Learning with Flow-based Policy
- QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
- QPKO: Differentiable QP-Embedded Deep Koopman Framework for Modeling Nonlinear Systems
- QPoint: End-to-End Lightweight Point Cloud Processing via Robust Quaternion Feature Learning
- Q-SAM: Unlocking Sharpness-Aware Minimization for Generalization in Offline Reinforcement Learning
- Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
- Q-Tab: Quantized Tabular Data Generator
- QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
- Quadratically Regularized Optimal Transport: Localization Bounds and Affine Case Analysis
- Quantifying and Optimizing Simplicity via Polynomial Representations
- Quantifying Biases in LLM-as-a-Judge Evaluations
- Quantifying Frontier LLM Capabilities for Container Sandbox Escape
- Quantifying LLM Attention-Head Stability: Implications for Circuit Universality
- Quantifying Temperature Scaling in Discrete Sequence (Language) Models
- Quantifying the Effect of Noise in Language Generation
- Quantifying the Generalization Gap in Seizure Detection: A Large-Scale Empirical Benchmark via the SzCORE Challenge
- Quantifying the noise sensitivity of the Wasserstein metric for images
- Quantile-Free Uncertainty Quantification in Graph Neural Networks
- Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning
- Quantized Maximum Likelihood Estimation under Normal Mean-Variance Mixture Model
- Quantum Algorithms for Triangle Cut Sparsification
- QuantumBoost: A lazy, yet fast, quantum algorithm for learning with weak hypotheses
- Quantum latent distributions in deep generative models
- Quantum Robust Inner Minimization for Reinforcement Learning with Quadratic Speed-Up in Query Complexity
- Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
- QuantWear: Quantum-scale Wear Particle Detection for Jet Engine Diagnosis
- QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
- Quaternion Self-Attention with Shared Scores
- QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning
- Query-Based Asymmetric Modeling with Decoupled Input–Output Rates for Speech Restoration
- Query Circuits: Explaining How Language Models Answer User Prompts
- Query-efficient model evaluation using cached responses
- Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
- Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better
- QuITE: Query-based Irregular Time-series Embedding
- R$^3$L: Reasoning 3D Layouts from Relative Spatial Relations
- R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
- R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
- R2-Router: A New Paradigm for LLM Routing with Reasoning
- RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference
- RaBiT: Residual Aware Binarization Training for Accurate and Efficient LLMs
- RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
- RADAR: Defending RAG Dynamically against Retrieval Corruption
- RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
- RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
- RADE: Unbiased Random Add-Drop Edge as a Regularizer
- Radial Scaling Voxelization for Accurate Small Object 3D Detection
- RADIO1D: Elastic Representations for Condensed Vision Modeling
- RAD: Retrieval High-quality Demonstrations to Enhance Decision-making
- RaGEP: Rank-aware Geometric Expert Pruning for Mixture-of-Experts Language Models
- RAG without Forgetting: Continual Query-Infused Key Memory
- RAIGen: Rare Attribute Identification in Text-to-Image Generative Models
- RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
- Ramba: Selective State-Space Models for Relational Deep Learning
- Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
- Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes
- Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
- Random Scaling of Emergence Capabilities
- Random Selection Reveals Implicit Knowledge Consensus in Code Generation
- Rank-Aware Spectral Bounds on Attention Logits for Stable Low-Precision Training
- Rank-guided Diffusion for Noise Few-Shot Learning
- Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
- Ranking Time Series using a Time Warping Ideal Point Model
- *Rank-Learner*: Orthogonal Ranking of Treatment Effects
- Rapid Poison: Practical Poisoning Attacks Against the Rapid Response Framework
- RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
- RapTB: Rooted Absorbed Trajectory Balance with Submodular Replay for Stable Autoregressive GFlowNet Training
- Rare Event Analysis of Large Language Models
- Rashomon Sets of Falling Trees
- RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
- Rate or Fate? RLV$^{\varepsilon}$R: Reinforcement Learning with Verifiable Noisy Rewards
- Rationality Measurement and Theory for Reinforcement Learning Agents
- Rational Neural Networks have Expressivity Advantages
- Rational Transductors
- Ratio-Variance Regularized Policy Optimization
- RAT+: Train Dense, Infer Sparse - Recurrence Augmented Attention for Dilated Inference
- RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation
- Rays as Pixels: Learning A Joint Distribution of Video and Camera Trajectories
- RBCBF: Decoding Time Safety Alignment via Risk Guided Rollback and Barrier Control
- RC-FCL: Combating Asynchronous Concept Drift in Federated Continual Learning via Retrospective Calibration
- R-Diverse: Mitigating Diversity Illusion in Self-Play LLM Training
- RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization
- Reading Between the Tokens: Improving Preference Predictions through Mechanistic Forecasting
- Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design
- ReaForest: Fostering Generative Video Reasoning for Spatial Planning
- Real Data Lies: Unveiling and Closing the Quality Shortcut in Generalizable AI-Generated Video Detection
- RealisMotion: Decomposed Human Motion Control and Video Generation in the World Space
- REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
- Realistic Adaptive Merging
- Realizable Bayes-Consistency for General Metric Losses
- REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
- REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment
- Real-Time Aligned Reward Model beyond Semantics
- Real-Time and Lightweight Diffusion Image Compression
- Real-Time Monitoring and Calibration of Chain-of-Thought Sycophancy in Large Reasoning Models
- RealtimeTool: Parallel Decoding for Real-Time LLM Function Calling
- Real-Time Visual Attribution Streaming in Thinking Model
- Real-World Unsupervised Models Generalize to Predict Brain Responses to Out-of-Distribution Stimuli
- REAR: Test-time Preference Realignment through Reward Decomposition
- ReasonEdit: Editing Vision--Language Models using Human Reasoning
- Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs
- Reasoning as an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs
- Reasoning Cache: Learning to Extrapolate to Long Lengths via Short-Length RL
- Reasoning Can Be Restored by Correcting a Few Decision Tokens
- Reasoning Compartmentalization: Bridging the Concretization Gap via Abstraction-based Routing
- Reasoning Is Not Free: Robust Adaptive Cost-Efficient Router for LLM-as-a-Judge
- Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
- Reasoning Models Are Test Exploiters: Rethinking Multiple Choice
- Reasoning Models Struggle to Control their Chains of Thought
- Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
- Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation
- Reasoning-preserved Efficient Distillation of Large Language Models via Activation-aware Initialization
- Reasoning Structure of Large Language Models
- Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
- Reasoning-VLA: An Efficient and Spatial-Guided General Vision-Language-Action Reasoning Model for Autonomous Driving
- Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning
- Reason with Thumbnails, Answer with Focus: An Efficient and Effective Paradigm for Multimodal Grounded Visual Reasoning
- ReAugment: Targeted Few-Shot Time Series Augmentation via Model Zoo-Guided Reinforcement Learning
- RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data
- Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems
- ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction
- Reconstructing Template-Memorized Images from Natural Prompts
- Reconstruction Outcomes Look Similar but Processes Differ: Improving Context Consistency and Coverage in Graph Masked Auto-Encoder
- Recontextualization Mitigates Specification Gaming Without Modifying the Specification
- Recovering Hidden Reward in Diffusion-Based Policies
- Recovering Policy-Induced Errors: Benchmarking and Trajectory Synthesis for Robust GUI Agents
- RECOVER:Reliable Detection of Unauthorized Data Usage in Text-to-Image Diffusion Models via Inversion Robustness
- Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations
- Rectifying Gradient Trajectories: A Hierarchical Geometric Framework with Structural Constraints for Few-Shot EEG Adaptation
- RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning
- Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data
- Recurrent Structural Policy Gradient for Partially Observable Mean Field Games
- Recursive Binding on a Budget: Subspace Carving in Order-$p$ Tensor Memories
- Recursive Models for Long-Horizon Reasoning
- Recursive Monte-Carlo Tree Search
- RedDebate: Safer Responses Through Multi-Agent Red Teaming Debates
- RED-HDP-HMM: Observation-Dependent Durations for Bayesian Nonparametric Sequential Models
- Reduction of Probabilistic Chemical Reaction Networks
- RedVisor: Reasoning-Aware Prompt Injection Defense via Zero-Copy KV Cache Reuse
- RefChess: Monte-Carlo Move Selection for Zero-Shot Referring Image Segmentation
- Reference-Free Meta-Learning for Generalized Implicit Neural Representation in Efficient MRI Reconstruction
- Referring Multiple Regions with Large Multimodal Models via Contextual Latent Steering
- Refined Analysis of Entropy-Regularized Actor-Critic
- RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience
- Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
- Refining Dual Spectral Sparsity in Transformed Tensor Singular Values
- Reflective Hamiltonian Monte Carlo: Mixing Analysis and Application to Sampling on Stiefel Manifold
- Reflector: Internalizing Step-wise Reflection against Indirect Jailbreaks
- Reflect-then-Correct: Rebalancing Task Optimization for Generalizable Meta-Reinforcement Learning via Distributional Value Error Reduction
- Reflex: Real-Time Vision-Language-Action Control through Streaming Inference
- ReflFlow: Learning Geometry-Guided Ray Tracing for Dynamic Specular Reconstruction
- Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
- ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
- Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
- REG: In-Sample RL via Regularizing the Evaluation Gap
- Regression Language Models for Code
- Regret-Based Federated Causal Discovery with Unknown Interventions
- Regret Minimization With a Crowd of Awakening Experts
- Regret Pre-training: Bridging Prior and Posterior Views for Enhanced Knowledge Grounding
- Regularization in the Axiomatic Approach to Learning from Human Preferences
- Regularized Discriminative Alignment for Deep Representations under Label Shift
- Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
- Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis
- Reinforced Sequential Monte Carlo for Amortised Sampling
- Reinforcement-aware Knowledge Distillation for LLM Reasoning
- Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
- Reinforcement Learning for Non-Verifiable Problems
- Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality
- Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
- Reinforcement Learning via Self-Distillation
- Reinforcement Learning with Action-Triggered Observations
- Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces
- Reinforcement Learning with Evolving Rubrics for Deep Research
- Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification
- Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
- ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
- ReLAM: Learning Anticipation Model for Rewarding Visual Robotic Manipulation
- Relational In-Context Learning via Synthetic Pre-training with Structural Prior
- Relational Structural Causal Models
- Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
- RelaxFlow: Text-Driven Amodal 3D Generation
- RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
- Relevance-Based Embeddings: Lightweight Candidate Selection via Heavy Ranker Calls
- Reliability-Aware LLM Alignment from Inconsistent Human Feedback
- Reliable Confidence Alignment for Generalized Category Discovery
- Reliable Neighborhood-Aware Multi-View Outlier Detection
- Reliable Thinking with Images
- Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
- RELO: Reinforcement Learning to Localize for Visual Object Tracking
- ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
- Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacers
- Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
- Removing Sandbagging in LLMs by Training with Weak Supervision
- ReNF: Rethinking the Principles of Neural Long-Term Time Series Forecasters
- Rényi Diffusion Models
- RePack then Refine: Efficient Diffusion Transformers with Vision Foundation Models
- Reparameterization Flow Policy Optimization
- Reparameterization Proximal Policy Optimization
- RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress
- rePIRL: Learn PRM with Inverse RL for LLM Reasoning
- Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following
- RePo: Language Models with Context Re-Positioning
- Representational Curvature Shapes Behavioral Uncertainty in Large Language Models
- Representational Similarity and Model Behavior in Multi-Agent Interaction
- Representation Drift Compensation: A Zero-Cost Enhancement for LLM Decomposition
- Representation Learning for Equivariant Inference with Guarantees
- Representation Unlearning: Forgetting through Information Compression
- RePro: Training Language Models to Faithfully Recycle the Web for Pretraining
- ReQAT: Achieving Full-Precision Reasoning Accuracy with 4-bit Floating-Point Quantization-Aware Training
- Required Spine Optional Limbs: Heterogeneous Federated Learning via Backbone-sharing and Activation-guided Selection
- Reranker Helps, but Not Enough: Towards Strong Poisoning Attacks Against Retrieval-Augmented Generation
- ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards
- Residual Context Diffusion Language Models
- RESIDUAL-GUIDED MULTI-RESOLUTION REFINEMENT OF FOUNDATION MODELS - A CASE STUDY IN DROUGHT FORECASTING
- Resilient Coresets and Consistent Clustering
- Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification
- Resolving Blind Inverse Problems under Dynamic Range Compression via Structured Forward Operator Modeling
- Resolving the Timestep Scaling Paradox in Spiking Neural Networks with a Timestep-Scalable Neuron Model
- Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement
- Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models
- ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
- Responsible Text-to-Image Diffusion: Interpretable and Linearly Controllable Semantics for Fair and Safe Generation
- ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning
- REST: Diffusion-based Real-time End-to-end Streaming Talking Head Generation via ID-Context Caching and Asynchronous Streaming Distillation
- Resting Neurons, Active Insights: Robustify Activation Sparsity for Large Language Models
- Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Models
- Restoring Initial Noise Sensitivity in Text-to-Image Distillation through Geometric Alignment
- ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
- Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
- Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
- Rethinking 3D Shape Generation: Diffusion over Superquadrics
- Rethinking Attention in Spiking Transformers: Overcoming Density Bias with Set Similarity
- Rethinking Calibration for Early-Exit Neural Networks
- Rethinking Code Complexity Through the Lens of Large Language Models
- Rethinking Contrastive Learning for Graph Collaborative Filtering: Limitations and A Simple Remedy
- Rethinking Convergence in MoE Training: The Role of Routing Sparsity
- Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective
- Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
- Rethinking Evaluation Paradigms in IBP-based Certified Training
- Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
- Rethinking Federated Prompt Learning for Medical Images: From Textual Tuning to Visual Manifold Anchoring
- Rethinking Forgery Attacks on Semantic Watermarks in Black-Box Settings: A Geometric Distortion Perspective
- Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
- Rethinking generative image pretraining: How far are we from scaling up next-pixel prediction?
- Rethinking Genomic Modeling Through Optical Character Recognition
- Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
- Rethinking Human Intent to CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning
- Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
- Rethinking LLM Ensembling from the Perspective of Mixture Models
- Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View
- Rethinking Low-Confidence Pseudo Labels: Influence-Aware Semi-Supervised Fine-Tuning for Hyperspectral Change Detection
- Rethinking Multimodal Time-Series Forecasting Evaluation
- Rethinking Neural Network Learning Rates: A Stackelberg Perspective
- Rethinking Parameter Sharing as Graph Coloring for Structured Compression
- Rethinking Personalization in Large Language Models at the Token Level
- Rethinking Pretraining Data Detection for LLMs: From Local to Global
- Rethinking Serialization in Linear 3D Vision: Decoupling Anisotropic Geometry from Isotropic Semantics
- Rethinking Sparse Mixture of Experts from a Unified Perspective
- Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence
- Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
- Rethinking the Flow-based Gradual Domain Adaption: A Semi-Dual Optimal Transport Perspective
- Rethinking the Hardness of PbRL: A Provable General Regret Bound
- Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation
- Rethinking the Trust Region in LLM Reinforcement Learning
- Rethinking Thinking Tokens: LLMs as Improvement Operators
- Rethinking Time-Series Imputation as Conditional Inference along Temporal Evolution
- Rethinking Video Generation Model for the Embodied World
- Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
- Rethinking Visual Intelligence: Insights from Video Pretraining
- Rethink the Role of Neural Decoders in Quantum Error Correction
- RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents
- Retrieval-Aware Distillation for Transformer-SSM Hybrids
- Retriever Portfolios: A Principled Approach to Adaptive RAG
- Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis
- RetrOrchestrator: A Multi-Step Retrosynthesis Agent Dynamically Orchestrating Single-Step Transition Models
- Return-Critic: Bridging Goal Discrepancy for Efficient Visual Reinforcement Learning
- Return of Frustratingly Easy Unsupervised Video Domain Adaptation
- Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
- Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
- Reusing Trajectories in Policy Gradients Enables Fast Convergence
- Revealing Behavioral Plasticity in Large Language Models: A Token-Conditional Perspective
- Revealing Differences in Multi-Modal Embeddings via Constrained Kernel Analysis
- Revealing Long-context Potential of Attention Heads via Frequency Kernels
- Revealing Scaling Behavior in Large-scale Time Series Models: Implications for More Efficient and Accurate Forecasting
- RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
- Revenue Efficiency of Correlated Equilibria in First Price Auctions
- Reverse-Engineering Model Editing on Language Models
- Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
- Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics
- Revisiting Asymmetries in Black-box Link Stealing against Graph Neural Networks
- Revisiting Coding-Based Approaches to Overcome the Curse of Dimensionality in Learning-Based Watermarking
- Revisiting Distribution Correction Estimation for Offline Imitation Learning with Suboptimal Dataset
- Revisiting Efficiency–Accuracy Scaling in Mixture-of-Experts Architectures
- Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
- Revisiting Neural Processes via Fourier Transform and Volterra Series
- Revisiting OOD Generalization in Programmatic RL
- Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
- Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
- Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
- Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
- Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation
- Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games
- Revisiting Robustness for LLM Safety Alignment via Selective Geometry Control
- Revisiting Spectral Representations in Generative Diffusion Models
- Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners
- Revisiting the Platonic Representation Hypothesis: An Aristotelian View
- Revisiting the Role of Pretrained Weights in Model Merging: On Near-Optimality within the Core Subspace
- Revisiting the Volume Hypothesis
- Revisiting Uncertainty: On Evidential Learning for Partially Relevant Video Retrieval
- Revisiting Zeroth-Order Hessian Approximation: A Single-Step Policy Optimization Lens
- REVIS: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models
- ReViT: Rotational-equivariant Vision Transformers for Neural PDE Solvers
- REViT: Roto-reflection Equivariant Convolutional Vision Transformer
- Reviving Error Correction in Modern Deep Time-Series Forecasting
- ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning
- Reward and Guidance through Rubrics: Promoting Exploration to Improve Multi-Domain Reasoning
- Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios
- Reward-free Alignment for Conflicting Objectives
- Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use
- Reward Learning through Ranking Mean Squared Error
- Reward Modeling from Natural Language Human Feedback
- Reward-Preserving Counterfactual State Editing for Offline Reinforcement Learning
- Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
- Reward Shaping Control Variates for Off-Policy Evaluation Under Sparse Rewards
- Reward Shaping for Inference-Time Alignment: A Stackelberg Game Perspective
- Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
- Rewiring Experts on the Fly: Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models
- Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
- RGGT: A Generative-Prior-Guided Transformer for Unified Rigid and Non-Rigid Point Cloud Registration
- RGMem: Renormalization Group–inspired Memory Evolution for Language Agents
- Rh-3DGS: Robust Open-Vocabulary Scene Understanding via Riemannian Huber Distillation and Manifold-Aware Sampling
- RiboSphere: Learning Unified and Efficient Representations of RNA Structures
- Richer Bayesian Last Layers with Subsampled NTK Features
- Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
- Riemannian Dueling Optimization
- Riemannian MeanFlow
- Riemannian MeanFlow for One-Step Generation on Manifolds
- Riemannian Metric Matching for Scalable Geometric Modeling of Distributions
- Riemannian Networks over Full-Rank Correlation Matrices
- Riemannian Neural Optimal Transport
- Riemannian Optimization for Fair Spectral Clustering
- Riemannian stochastic optimization for sufficient dimension reduction
- Ripple Perturbations Through Structure: Likelihood-Constrained Adversarial Attacks on Heterogeneous Tabular Data
- Risk-Averse and Optimistic Advertiser Incentive Compatibility in Auto-bidding
- Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility
- Risk-Bounded Distribution Reconstruction: Stable Statistic Calibration for Long-Tailed Recognition
- RiskZero: Plan More to Risk Less with a Learned Model
- RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms through Curriculum Design and Graph-based Search
- RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
- RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks
- RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
- RLSF-V: Mitigating Hallucinations in MLLMs via Fuzzy Semantic Self-Feedback
- RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs
- RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
- RLxF: RL from World Feedback
- RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization
- RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
- RN-D: Discretized Categorical Actors with Regularized Networks for On-Policy Reinforcement Learning
- ROAMM: A Benchmark Dataset for Multimodal Human Attention Decoding and EEG-to-Text Modeling During Naturalistic Reading
- RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation
- RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
- RoboOmni: Actions Are Just Another Modality for Your Vision-Language Models
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
- Robust AI Evaluation through Maximal Lotteries
- Robust and Consistent Ski Rental with Distributional Advice
- Robust Bayes-Assisted Conformal Prediction
- Robust Bayesian Optimisation with Unbounded Corruptions
- Robust Causal Discovery in Real-World Time Series with Power-Laws
- Robust Contextual Optimization with Missing Covariates
- Robust Cross-Modal Retrieval via Generative Semantic Refinement and Exclusion-Guided Adaptation
- Robust Federated Learning Against Adaptive Compression
- Robust Filter Attention: Self-Attention as a Parallel State Estimator
- Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models
- Robustifying Vision-Language Models via Test-Time Prompt Adaptation
- Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks
- Robust Inter-Series Dependency Modeling for Time Series Forecasting via Information-Theoretic Alignment
- Robust Learning via Nested Distributionally Robust Optimization
- Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
- Robust Multi-View Fusion via Prototype-Anchored Unbalanced Optimal Transport
- Robustness of Mixtures of Experts to Feature Noise
- Robust Parallel Diffusion Sampling via Dynamic Jacobian Bandwidth
- Robust Self-reflective Hashing for Cross-modal Retrieval with Noisy Label
- Robust Sequential Experimental Design for A/B Testing
- Robust Signal Enhancement via Fractional Detail Views and Knowledge Guided Multi-view Fusion
- Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
- Robust Strategic Classification under Decision-Dependent Cost Uncertainty
- Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?
- Robust Vision-Language Models via Manifold-Adversarial Adapters
- RoCA: Robust Cross-Domain End-to-End Autonomous Driving
- Role-Level Inductive Bias for Cross-Task Generalization in Multi-Agent Reinforcement Learning
- Romberg-Extrapolated Zeroth-Order Gradient Estimator: Higher-Order Bias Reduction with Preserved Leading Directional Variance
- Root Cause Analysis of Failures in Microservices via Bayesian Root Cause Discovery
- Rotary Position Encodings for Graphs
- Rotation-Invariant Spherical Watermarking via Third-Order SO(3) Representation Coupling
- RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing
- Routing and Reasoned Evaluation with Large Language Models
- Row-stochastic matrices can provably outperform doubly stochastic matrices in decentralized learning
- RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
- RSA-CP: Efficient Conformal Prediction in Small-Sample Regimes via Random Score Alignment
- RSAgent: Learning to Reason and Act via Multi-Turn Tool Invocations for Text-Guided Segmentation
- RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
- RSPO: Regularized Self-Play Alignment of Large Language Models
- RSTR: Reducing SpatioTemporal Redundancy in Diffusion Transformers
- RTInfer: Exploiting Concurrency for Multiple Real-Time DNN Inference on Edge GPUs
- RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models
- RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference
- Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Creative Writing
- RubricRobustness: A Simple Framework for Evaluating the Robustness of Rubrics-Based Benchmarks
- RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning
- Rule2DRC: Benchmarking LLM Agents for DRC Script Synthesis with Execution-Guided Test Generation
- RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning
- RVAS: Referring Video Active Exploration and Segmentation
- S$^2$MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
- S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
- S2GS: Streaming Semantic Gaussian Splatting for Online Scene Understanding and Reconstruction
- S3Audio: Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer
- SABER: Continual Learning with Representation Conflict Management
- SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
- SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders
- SAEs-BrainMap: Unveiling the Emergence of Specialized Concepts in Deep Models via Brain Alignment
- Safe and Scalable Web Agent Learning via Recreated Websites
- Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks
- SafeCompass: Dynamic Chain-of-Thought Steering via Inference-Time Safety Signals
- SafeDec: Constrained Decoding for Safe Autoregressive Generalist Robot Navigation Policies
- Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
- SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety
- Safe In-Context Reinforcement Learning
- SafeLab: An Interactive High-Fidelity Benchmark for Embodied Safety in Scientific Robotics
- Safe Reinforcement Learning with Preference-based Constraint Inference
- SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond
- SafeSearch: Automated Red-Teaming of LLM-Based Search Agents
- SafeSeek: Universal Attribution of Safety Circuits in Language Models
- SafeSpec: Fast and Safe LLM via Dynamic Reflective Sampling
- Safety Alignment of LMs via Non-cooperative Games
- Safety Anchor: Defending Harmful Fine-tuning via Geometric Bottlenecks
- Safety-Efficacy Trade Off: Robustness against Data-Poisoning
- Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization
- Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed
- Safety Recovery in Reasoning Models Is Only a Few Early Steering Steps Away
- SAGE: A Dataflow-Native Framework for Modular, Controllable, and Transparent LLM-Augmented Reasoning
- SAGE-NAS: Synergizing LLM-Based Semantic Agent with Graph-Based Evaluator for Neural Architecture Search
- SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs
- SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
- SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling
- Saliency-Aware Model Merging
- SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos
- Salus: Strategic Diagnostic Testing for Complex Diagnosis via Multi-Agent Reinforcement Learning
- SAM Audio: Segment Anything in Audio
- Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
- Same Question, Different Lies: Cross-Context Consistency (C³) for Black-Box Sandbagging Detection
- SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
- Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
- Sampled hard labels from sparse targets mislead rotation invariant algorithms
- Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
- Sample Efficient Full-Finetuning of Generative Control Policies
- Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
- Sample Margin-Aware Recalibration of Temperature Scaling
- Sampling and Identity-Testing Without Approximate Tensorization of Entropy
- Sampling from Your Language Model One Byte at a Time
- SAMT: Generating Structured Avatar Meshes and Textures from a Single Image
- SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
- SAQNN: Spectral Adaptive Quantum Neural Network as a Universal Approximator
- SARL: Structure-Aligned Reinforcement Learning for Bridging the Perception-Action Gap in Airspace
- SARSteer: Safeguarding Large Audio Language Models via Safe-Ablated Refusal Steering
- SaTeen: Learning Structural Alignment for Continual Test-Time Adaptation
- Saving Foundation Flow-Matching Priors for Inverse Problems
- SC$^{2}$-WM: A Self-Correcting World Model with Closed-Loop Feedback for Vision-and-Language Navigation in Continuous Environments
- Scalable and Differentiable Point-Cloud Registration Using Maximum Mean Discrepancy
- Scalable and General Whole-Body Control for Cross-Humanoid Locomotion
- Scalable and Interpretable Representation Alignment with Ordinal Similarity
- Scalable and Stable Estimation of Amari $\alpha$-Divergence using Random Fourier Features
- Scalable Bayesian Inference for Nonlinear Conservation Laws
- Scalable Bayesian Semi-supervised Clustering with Feature Selection and Adaptive Constraint Weighting
- Scalable Event Cloud Network for Event-based Classification
- Scalable GANs with Transformers
- Scalable Kronecker-Factored Fisher Approximation for Neural Network Parameter Sensitivity
- Scalable Medical Multimodal Fusion via Symmetric Consistency Modeling
- Scalable Option Learning in High-Throughput Environments
- Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening
- Scalable RF Simulation in Generative 4D Worlds
- Scalable Sampling via Generalized Fixed-Point Diffusion Matching
- Scalable Simulation-Based Model Inference with Test-Time Complexity Control
- Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
- Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
- Scalable Traffic Signal Control with Shared Policy Framework
- Scalable Training of 3D Gaussian Splatting via Out-of-Core Optimization
- SCalDA: Semantics-Calibrated and Diffusion-Enhanced Data Augmentation
- Scale-Aware Domain Harmonization for Domain Adaptation Person Search
- ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training
- ScaleErasure: Inference-Time Minimal Intervention for Precise Concept Erasure in Next-Scale Autoregressive Image Generation
- ScaleMoE: Mixture-of-Experts for Scalable Continuous Control in Actor-Critic Reinforcement Learning
- SCALE: SCALABLE LEARNING AND OPTIMIZATION FOR EFFICIENT MULTIMODAL AI AGENTS
- SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models
- ScaleSim: Serving Large-Scale Multi-Agent Simulation with Invocation Distance-Based Memory Management
- Scaling Agentic Verifier for Competitive Coding
- ScalingAR: Scaling Confidence for Autoregressive Image Generation
- Scaling-Aware Adapter for Structure-Grounded LLM Reasoning
- Scaling, Benchmarking, and Reasoning of Vision-Language Agents for Mobile GUI Navigation
- Scaling Beyond Masked Diffusion Language Models
- Scaling by Diversified Experience for Vision-Language-Action Models
- Scaling Continual Learning with Bi-Level Routing Mixture-of-Experts
- Scaling Generative Verifiers For Natural Language Mathematical Proof Verification And Selection
- Scaling Inference-Time Computation via Opponent Simulation: Enabling Online Strategic Adaptation in Repeated Negotiation
- Scaling Law for Quantization-Aware Training
- Scaling Laws and Architectural Frontiers in Metagenomic Foundation Models
- Scaling Laws for Precision in High-Dimensional Linear Regression
- Scaling Laws in Model Fine-tuning for Audio DeepFake Detection
- Scaling Laws of Global Weather Models
- Scaling Long-Horizon Agent via Context Folding
- Scaling Multi-Agent Environment Co-Design with Diffusion Models
- Scaling Prompt Synthesis for Large Language Model Reasoning
- Scaling Real-World Robot Policy Evaluation via Discrete Diffusion World Model
- Scaling Small Agents Through Strategy Auctions
- Scaling the Prior: Size-Consistent Geometric Diffusion for 3D Molecular Generation
- Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning
- Scaling Transformers for End-to-End Discrete Audio Tokenization
- Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization
- Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers
- Scaling Vision Transformers for Functional MRI with Flat Maps
- ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning
- Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs
- scCBGM: Single-Cell Editing via Concept Bottlenecks
- scChord: A Probabilistic Manifold Rectification Framework for RNA-to-Protein Translation
- scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics
- scDEBART: Predicting in silico Single-Cell Perturbation Responses via Large-Scale Differential Expression Learning
- ScDiVa: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression
- SceneDirector: Bridging Explicit Geometry and Generative Priors for Unified Driving Scene Editing
- Scene Graph Thinking: Reinforcing Structured Visual Reasoning for Multimodal Large Language Models
- ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving
- SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes
- SC-FAGC: Size Constrained Fast Anchor-based Graph Clustering
- Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions
- Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models
- Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics
- Schur-A*: Layer-wise Optimal Expert Pruning for Sparse MoEs via Schur-Complement Guided A* Search
- SciAgentGym: Benchmarking Multi-Step Scientific Tool-Use in LLM Agents
- Scientific logicality enriched methodology for LLM reasoning: A practice in physics
- SciNet: Evaluating AI Agents in Relation-Aware Scientific Literature Retrieval
- SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?
- SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models
- SCNS: Continual Personalization of Diffusion Models via Submodular Concept Neuron Selection
- SCoA: Revisiting Domain Generalized Object Detection with Style-Conditioned Adaptation
- SCOPE and SCION: Benchmark and Method for Ontology Induction and Fusion from Text
- SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
- SCOPE: Selective Conformal Optimized Pairwise LLM Judging
- SCORE: A Unified Framework for Overshoot Refund in Online FDR Control
- Score Based Error Correcting Code Decoder
- ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
- ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Face Recognition
- Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
- SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States
- Scout Before You Attend: Sketch-and-Walk Sparse Attention for Efficient LLM Inference
- SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
- SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation
- SDiD：Shared diffusion prior for efficient distributed stereo image compression
- SDM: A Powerful Tool for Evaluating Model Robustness
- SD-MoE: Spectral Decomposition for Effective Expert Specialization
- SE(3)-Equivariant Flow Matching with Gaussian Process Priors for Geometric Trajectory Prediction
- SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
- Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories
- Search or Accelerate: Confidence-Switched Position Beam Search for Diffusion Language Models
- Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration
- Search Space Synthesis for Parametric Functions
- SecCodePRM: A Process Reward Model for Code Security
- Second-Order Bilevel Optimization with Accelerated Convergence Rates
- Second-Order Smooth Planning with Optimal-Transport Bellman Smoothing
- Second Pluralistic Alignment Workshop
- Second Workshop of AI4NextG: AI and ML for Next-Generation Wireless
- Second Workshop on Technical AI Governance Research
- Secure Multi-agent Reinforcement Learning for Service Systems with Affinity and Byzantine Nodes: Stability Analysis and Protection Design
- Securing Multimodal AI through Internal Information Decomposition
- Security–Fidelity Tradeoffs: No Universal Defense Against Prompt Injection
- SEDRAS: Symbolically Evaluated Deep Research And Science
- See, Act, Adapt: Active Perception for Unsupervised Cross-Domain Visual Adaptation via Personalized VLM-Guided Agent
- See First, Reason Later: Mutual Information-Guided Reinforcement Learning for Vision-Language Models
- Seeing is Solving: Unlocking Efficient Multimodal RL via View Alignment
- Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs
- Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation
- Seeing Symbols, Missing Structure: A Real-World Handwritten Mathematical Expression Recognition Benchmark for Large Models
- Seeing the Unseen: Physics-as-Representation for Generalizable Gaze Perception
- Seeing to Generalize: How Visual Data Corrects Binding Shortcuts
- Seeing Without Understanding: Disentangling Perception, Reasoning, and Simulation in VLM Gameplay
- Seeking Commonality, Preserving Specificity: A Spectral-Aware Hierarchical Framework for Cross-City Road Representation Learning
- See More, Forecast Better and Faster: Enhancing Time Series Foundation Models via Inference-Time Plug-and-Play Downsampling
- SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
- See the Emotion: A Facial Emoji Proxy Modeling for EEG Emotion Recognition
- See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model
- SE-GA: Memory-Augmented Self-Evolution for GUI Agents
- Segment-Aligned Policy Optimization for Multi-Modal Reasoning
- Segment Anything with Robust Uncertainty-Accuracy Correlation
- Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation
- SegPVSG: Panoptic Video Scene Graph Generation via Temporal Focusing and Generative Augmentation
- Seg-ReSearch: Segmentation with Interleaved Reasoning and External Search
- SeisMark: A Large-Scale Open Benchmark for Robust 3D Seismic Fault Detection
- Seizure-Semiology-Suite($S^3$): A Clinically Multimodal Dataset, Benchmark, and Models for Seizure Semiology Understanding
- Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration
- Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers
- Selective Deferred Routing: Enabling Cost-Efficient Collaboration between Local SLMs and Remote LLMs
- Selective Disclosure Watermarking for Large Language Models
- Select to Think: Unlocking SLM Potential with Local Sufficiency
- Self-Augmenting Retrieval for Diffusion Language Models
- Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models
- Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models
- Self-correcting for Debiasing Large Language Models
- Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation
- Self-Distillation Enables Continual Learning
- Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
- Self-Evolving LLM Agents under Offline Data Support
- Self-Guidance: Enhancing Neural Codecs via Decoder Manifold Alignment
- SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verification
- Self Optimizing Language Models
- Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Edit via In-Context Learning
- Self-Prophetic Decoding to Unlock Visual Search in LVLMs
- Self-Refining Video Sampling
- Self-Soupervision: Cooking Model Soups without Labels
- Self-Supervised Dynamical System Representations for Physiological Time-Series
- Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
- Self-Supervised Foundation Model for Calcium-imaging Population Dynamics
- Self-supervised Hierarchical Visual Reasoning with World Model
- Self-Supervised Learning as Discrete Communication
- Self-Supervised Weight Templates for Scalable Vision Model Initialization
- Selling Data as a Digital Good with Scaling Valuations
- SEMA: a Scalable and Efficient Mamba like Attention via Token Localization and Averaging
- Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation
- Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching
- Semantic Editing with Coupled Stochastic Differential Equations
- Semantic-Enriched Latent Visual Reasoning
- Semantic Granularity Navigation in Image Editing
- Semantic Impact–Driven Visual Scheduling in Vision-Language Models
- Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression
- Semantic-level Backdoor Attack against Text-to-Image Diffusion Models
- SemanticNVS: Improving Semantic Scene Understanding in Generative Novel View Synthesis
- Semantic Robustness Certification for Vision-Language Models
- Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation
- Semantic Tube Prediction: Beating LLM Data Efficiency with JEPA
- SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks
- Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
- Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees
- Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares
- SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
- Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning
- Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
- Semi-Supervised Learning with Noisy Covariates: Generalization Bounds and Distribution Regression
- Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations
- Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
- SemRep: Code Transformation with Semantics-Preserving Representations
- SENDAI: A Hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework
- SE(n)-Invariant Flow Matching: A General Framework with Application to Object Reassembly
- Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
- Separating representation from reconstruction enables scalable text encoders
- SEPS: Semantic-Enhanced Patch Slimming Framework for Fine-Grained Cross-Modal Alignment
- Sequential Group Composition: A Window into the Mechanics of Deep Learning
- Sequential Kernel-based Conditional Independence Testing via Adaptive Betting
- SERA: Soft-Verified Efficient Repository Agents
- Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer
- Set-Coupled Guidance: Set-Level Coordination in Diffusion-Based Dataset Distillation
- Set Diffusion: Interpolating Token Orderings between Autoregression and Diffusion for Fast and Flexible Decoding
- SetPO: Set-Level Policy Optimization for Diversity-Preserving LLM Reasoning
- Set-Preserving Calibration from Conformal P-Values to E-Values
- SFCLTA: Spectral Fusion Contrastive Learning with Topology-Adaptive Graph Augmentation
- SFedPO: Streaming Federated Learning with a Prediction Oracle under Temporal Shifts
- SF-Mamba: Rethinking State Space Model for Vision
- SG2Loc: Sequential Visual Localization on 3D Scene Graphs
- SGERA: Stein-Guided ECG-Report Alignment for ECG Representation Learning
- SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
- Sham Kakade
- ShapCCS: Shapley-Driven Client Coreset Selection in Federated Learning
- Shape of Thought: Progressive Object Assembly via Visual Chain-of-Thought
- SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning
- Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
- Shapley Regularized Neural Granger Causality
- Shared Semantics, Divergent Mechanisms: Unsupervised Feature Discovery by Aligning Semantics and Mechanisms
- Sharp Concentration Bounds for Vector Bundle-Valued Statistics on Manifolds
- Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
- Sharp empirical Bernstein inequalities for the variance of bounded random variables
- Sharper Generalization Guarantees for Asynchronous SGD: Beyond Lipschitzness, Smoothness and Data Homogeneity
- Sharp Inequalities between Total Variation and Hellinger Distances for Gaussian Mixtures
- Sharpness-Aware Minimization Can Hallucinate Minimizers
- Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
- SHARP-Q: Spectral Hessian Alignment and Rectification for Post-training Quantization
- Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
- SHERPA: Fine-tuning Segment Anything Models with Task-relevant Guidance
- Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
- Shifting the Breaking Point of Flow Matching for Multi-Instance Editing
- SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass
- Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization
- Shortcut-Resistant CAM Distillation for Long-Tailed Recognition
- Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control
- Show, Don't Tell: Morphing Latent Reasoning into Image Generation
- Shrinking the Variance: Shrinkage Baselines for Reinforcement Learning with Verifiable Rewards
- Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation
- Shuffling-Aware Optimization for Private Vector Mean Estimation
- SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm
- SIGMA-PPG: Statistical-prior Informed Generative Masking Architecture for PPG Foundation Model
- Signal Strength Estimation in Logistic Regression Using Data Splitting
- Signature-Informed Transformer for Asset Allocation
- Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
- SI-IGCL: Subject Invariance-aware Inverse Graph Contrastive Learning for Psychiatric Disorder Identification
- SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
- SilentWood: Efficient Private Inference Over Gradient Boosting Decision Forests
- SimGFM: Simplifying Discrete Flow Matching for Graph Generation
- Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models
- SIMoE: A Probabilistic Framework for Cardinality-Constrained Routing in Mixture-of-Experts
- SIMPC: Learning Self-Induced Mirror-Point Consistency for Unsupervised Point Cloud Denoising
- Simple Algorithms for Bad Triangle Transversals with Applications to Correlation Clustering
- SimpleGPT: Improving GPT via A Simple Normalization Strategy
- SimpleMem: Efficient Lifelong Memory for LLM Agents
- Simple Policy Gradients for Reasoning with Diffusion Language Models
- Simple Unbiased Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
- Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
- SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs
- Simultaneous Confidence Bounds for Aggregated Effects via Exact Subset Optimization
- Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards
- Simultaneous Speech-to-Speech Translation Without Aligned Data
- Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws
- Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection
- Singular Bayesian Neural Networks
- Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization
- Singular Proxies for Adaptive Caching in Diffusion Language Models
- Singular Vectors of Attention Heads Align with Features
- Sinkhorn Treatment Effects
- SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights
- SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models
- Size Transferability of Graph Convolutional Networks across Sparsity: A Generalized Graphon Perspective
- SJD-SV: Speculative Jacobi Decoding with Semantics Verification for Autoregressive Image Generation
- SkelHCC: A Hyperbolic CLIP-Driven Cache Adaptation Framework for Skeleton-based One-Shot Action Recognition
- Sketch-Based Low-Rank Model Merging with Shared Circulant Transforms
- SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
- Skewness-Robust Causal Discovery in Location-Scale Noise Models
- Skill Neologisms: Towards Skill-based Continual Learning
- SkillNet: Hierarchical Skill Modeling for Compositional Generalization in Vision-Language Action Models
- SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems
- Skip a Layer or Loop It? Learning Program-of-Layers in LLMs
- Skip-It? Theoretical Conditions for Layer Skipping in Vision–Language Models
- Skipping the Zeros in Diffusion Models for Sparse Data Generation
- Ski Rental with Distributional Predictions of Unknown Quality
- SlaClip: Gradient Norm Slacks can be Indicator for Adaptive Clipping in DP-SGD
- SLAE: Strictly Local All-atom Environment for Protein Representation
- SLAP: The Semantic Least Action Principle for Variational Video-Language Modeling
- Slash the Sink: Sharpening Structural Attention Inside LLMs
- SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
- SleepLM: Natural-Language Intelligence for Human Sleep
- SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
- SlerpFlow: Spherical Trajectory Correction for Rectified Flow Inversion
- SliceFine: The Universal Winning-Slice Hypothesis for Pretrained Networks
- SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity
- SLIM: Secure and Efficient Inference for Large Language Models on Untrusted Devices via TEEs
- SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection
- SLQ: Bridging Modalities via Shared Latent Queries for Retrieval with Frozen MLLMs
- SL-VC: A Benchmark and Automated Framework for Separation Logic Verification Condition Proving
- SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer
- Small Agent Group is the Future of Digital Health
- Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO
- Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
- SMART: Scalable Mesh‑free Aerodynamic Simulations from Raw Geometries using a Transformer‑based Surrogate Model
- SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning
- SMD: Multi-view Safety-Critical Driving Video Generation in the Real-world Domain
- SMILE: Extended Deep Submodular Function-Based Instruction and In-context Learning Demonstration Selection
- SMM Transformer: Leveraging Spiking Neural Networks for Multimodal Tasks
- Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
- Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation
- Smoothing Slot Attention Iterations and Recurrences
- Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
- Smoothness Errors in Dynamics Models and How to Avoid Them
- SmoothSpike: Spiking Transformer with Learnable Hadamard Transformation
- Sobolev Regularized Score Difference Estimation in Diffusion Models
- Social Hippocampus Memory Learning
- SoftBinary Coding: A New Information-Theoretic Paradigm for Neural Compression via Fast Channel Simulation
- SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients
- Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective
- SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
- Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models
- Softsignum: Smooth Your Signum For Better Heterogeneity Handling
- SOLAR for Offline MARL: Plateau-Triggered Potential Shaping under World-Model Uncertainty
- SOLAR: Self-supervised Joint Learning for Symmetric Multimodal Retrieval
- Solver-in-the-Loop: MDP-Based Benchmarks for Self-Correction and Behavioral Rationality in Operations Research
- Solving Imperfect-Recall Games via Sum-of-Squares Optimization
- Solving Inverse Problems with Flow-based Models via Model Predictive Control
- Solving Physics Olympiad via Reinforcement Learning on Physics Simulators
- Solving Positive Linear Programs with Differential Privacy
- Solving Spatial-Spectral Fusion with Latent Spectral Operators
- Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
- Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach
- Solving Time-Dependent Differential Equations with Physical Dynamical Systems
- SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-Body Manipulation
- ``Someone Hid It!'': Query-Agnostic Black-Box Attacks on LLM-Based Retrieval
- SONAR: Spectral‑Contrastive Audio Residuals for Generalizable Deepfake Detection
- Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
- SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering
- SOPE: Situation-Aware and Statistically Indistinguishable Privacy Exfiltration for MCP-enabled Agents
- SORA: Free Second Order Attacks in Fast Adversarial Training
- SorryDB: Can AI Provers Complete Real-World Lean Theorems?
- SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
- Source-Free Open-World RF Fingerprint Identification
- SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection
- SpaCeFormer: Space-Curve Transformer for Open-Vocabulary 3D Instance Segmentation without Proposals
- SpaceVista: All-Scale Visual Spatial Reasoning from mm to km
- SPADA: A Verifiable Test-Driven Agent for Controllable Parametric CAD Assembly Generation
- SpaEF: Spatially Resolved Transcriptomics Data Element-Wise Denoising Framework Powered by Large Models
- SpanNorm: Reconciling Training Stability and Performance in Deep Transformers
- SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs
- SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance–Diversity Data Selection
- SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs
- SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
- Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agents
- Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
- Sparse and Faithful Local Explanations with Piecewise Linear Surrogates
- (Sparse) Attention to the Details: Preserving Spectral Fidelity in ML-based Weather Forecasting Models
- Sparse Autoencoders are Topic Models
- Sparse Autoencoders for Interpretable Emotion Control in Text-to-Speech
- Sparse Bayesian Deep Functional Learning with Structured Region Selection
- Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
- Sparse by Design: Relevance-Driven Scaling for Recommender Systems
- SparseInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
- Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
- SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training
- Sparser Block-Sparse Attention via Token Permutation
- Sparse Regression with $\ell_0$ Constraints for $\alpha$-Mixing Time Series: Algorithms and Guarantees
- Sparse Relaxed-Lasso Steering: Automatic Sparse-Autoencoder Feature Selection for Precise Image Editing
- Sparser, Faster, Lighter Transformer Language Models
- SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot
- Sparse Tokens Suffice: Jailbreaking Audio Language Models via Token-Aware Gradient Optimization
- Sparse Topology-Aware Pairwise Scoring for Large-Scale Multi-Agent Reinforcement Learning
- SPAR: Support-Preserving Action Rectification
- Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models
- Spatial Conformal Inference through Localized Quantile Regression
- Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
- SpatialJB: How Text Distribution Art Becomes The "Jailbreak Key" for LLM Guardrails
- Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization
- Spatially-Regularized Entropy for Discriminative Token Merging in Fine-Grained Re-Identification
- Spatial Memory for Out-of-Vision Manipulation in Vision-Language-Action
- Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers
- SpatialReward: Bridging the Perception Gap in Online RL for Image Editing via Explicit Spatial Reasoning
- SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
- SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
- Spatiotemporal Imputation with Graph-Informed Flow Matching
- Spatio-Temporal LLM: Reasoning about Environments and Actions
- SPEAR: A Unified SSL Framework for Learning Speech and Audio Representations
- SpecExit: Accelerating Large Reasoning Model via Speculative Exit
- SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
- SpecMD: A Comprehensive Study On Speculative Expert Prefetching
- SpecPL: Disentangling Spectral Granularity for Prompt Learning
- SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning
- Spectral Bridge Variational Inference: Dynamic LoRA via Bures-Wasserstein Gradient Flows
- Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning
- Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
- Spectral Flow Matching: Stabilizing Stochastic GFlowNets via Frequency-Domain Regularization
- Spectral Gradient Descent Mitigates Anisotropy-Driven Misalignment: A Case Study in Phase Retrieval
- Spectral Guidance for Flexible and Efficient Control of Diffusion Models
- Spectral Heat Flow for Conservative Token Condensation in Vision-Language Models
- Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation
- Spectral-Informed Neural Networks Outperform Spectral methods in High-dimensional PDEs
- Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning
- Spectral Reach: Understanding Neural Scaling through Kernel Alignment Dynamics
- Spectral–Spatial Mixing with Morphology-Aware Adaptive Loss for Medical Image Segmentation.
- Spectra: Rethinking Optimizers for LLMs Under Spectral Anisotropy
- Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation
- Speculative Safety Honeypot: Toward Proactive Defense Against Multi-turn Agent Attacks
- Speculative Sampling For Faster Molecular Dynamics
- Speech-Audio Compositional Attacks on Multimodal LLMs and Their Defense with SALMONN-Guard
- SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding
- SpeedCP: Fast Kernel-based Conditional Conformal Prediction
- SPEED: Sharpened-Teacher Distillation for Parallel Decoding of Diffusion Language Models
- Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
- SpeedVFI: One-step Diffusion for Efficient Video Frame Interpolation
- SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
- SphericalDreamer: Generating Navigable Immersive 3D Worlds with Panorama Fusion
- Spherical Procrustes Alignment for Reliable Medical Audio Diagnosis
- Spherical SO(3) Equivariant Local Attention
- Spherical Steering: Geometry-Aware Activation Rotation for Language Models
- Spik4lite: Refactoring Neuromorphic Sparsity for Efficient Spiking Neural Networks on Commodity Edge Devices
- Spike Camera Autofocus via Frequency-Domain Spectral-Centroid Migration
- SpikeCLR: Self-Supervised Contrastive Learning for Visual Representations with Spiking Neural Networks
- Spiked-CFR: Causal Representation Learning from LLMs via Wasserstein Projection Pursuit
- Spike-HTR: Spiking Neural Transformer for Handwritten Text Recognition
- SpikeNet: Sparse Spike-Driven Mask Vector Transformer for Energy-Efficient and Stable Spiking Point Cloud Processing
- SpikeVLA: Vision-Language-Action Models with Spiking Neural Networks
- SpikingLM: Towards Fully Spiking Language Model
- Spiral RoPE: Rotate Your Rotary Positional Embeddings in the 2D Plane
- SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion
- Split Group Knockoffs: Controlling False Discovery Rate in Transformational Group Sparsity
- Split Personality Training: Revealing Latent Knowledge Through Alternate Personalities
- SPLIT-VLM: Salience-Guided Partitioning towards Local Coverage for Importance-Aware Token Dropping in Vision-Language Models
- SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis
- Sponge Tool Attack: Stealthy Denial-of-Efficiency against Tool-Augmented Agentic Reasoning
- SPR: A Structured Prompt Refinement Network for Modality Missing
- SpreadsheetArena: Decomposing Preference in LLM Generation of Spreadsheet Workbooks
- SPR-RAFT: Parameter-Efficient Regression-Aware Fine-Tuning for Biomedical LLM Regression
- Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training
- Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs
- Spurious Rewards: Rethinking Training Signals in RLVR
- SPUR: Scale-Partitioned Uncertainty Rectification for Robust UAV-on-UAV Interception
- S-Quant: Rethinking Weight Quantization with Seed-Based Generation
- Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning
- SRPO: Self-Reflective Policy Optimization for Long-Horizon Reasoning
- SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space
- SSDCN: Spatial-Spectral Dual-Clustering-based Network for Hyperspectral Image Super-resolution
- SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning
- SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models
- SS‑TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models
- Stability Analysis of Sharpness-Aware Minimization
- Stability and Generalization of Nonconvex Optimization with Heavy-Tailed Noise
- Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text
- Stability beyond bounded differences: sharp generalization bounds under finite $L_p$ moments
- Stabilized Supralinear Networks Learn to Switch Coding Strategies Balancing Cost and Performance
- Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls
- Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
- Stabilizing Native Low-Rank LLM Pretraining
- Stabilizing PPO via Latent-Space Regularization and KDE-Driven Exploration
- Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models
- Stabilizing Reinforcement Learning for Diffusion Language Models
- Stabilizing the Q-Gradient Field for Policy Smoothness in Actor-Critic Methods
- Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs
- Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
- Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
- StableI2I: Spotting Unintended Changes in Image-to-Image Transition
- Stable Localized Conformal Prediction via Transduction
- STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics–Physics Dual System
- Stable Spectral Copula Alignment for Robust Multimodal Learning
- STABLEVAL: Disagreement-Aware and Stable Evaluation of AI Systems
- Stable Velocity: A Variance Perspective on Flow Matching
- StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
- Stage-wise Distortion–Perception Traversal in Zero-shot Inverse Problems with Diffusion Models
- STAND: Self-Aware Precondition Induction for Interactive Task Learning
- STARCaster: Spatio-Temporal AutoRegressive Video Diffusion for Identity- and View-Aware Talking Portraits
- Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control
- StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars
- STARE: Step-wise Temporal Alignment and Red-teaming Engine for Multi-modal Toxicity Attack
- STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control
- STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
- STAR-VAE: Structured Topology-Aware Regularization for Audio Reconstruction and Generation
- State-Dependent Safety Failures in Multi-Turn Language Model Interaction
- State Space Model with Continuous Limit of HiPPO Matrix: Eigenvalue Analysis and Explicit Solution Formula
- Stationary MMD Points
- Statistical Consistency and Generalization of Contrastive Representation Learning
- Statistical Early Stopping for Reasoning Models
- Statistical Frameworks for Uncertainty in Agentic Systems
- Statistical Learning Theory in Lean 4: Empirical Processes from Scratch
- Statistically Optimal Scaling for Token Merging in Transformers
- Statistically Undetectable Backdoors in Deep Neural Networks
- STD-Former: Image-Conditioned Texture Dictionary Encoding with Sparse Topological Supervision for Texture Recognition
- Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
- Steal the Patch Size: Adversarially Manipulate Vision Language Models
- Steering at the Source: Style Modulation Heads for Robust Persona Control
- Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation
- Steering Large Language Models through the DMTA Cycle: Structure-Based Drug Design via Knowledge-Driven Bi-Level Thompson Sampling
- Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning
- SteeringSafety: Benchmarking Representation Steering in LLMs Across Safety Perspectives
- Steer Like the LLM: Activation Steering that Mimics Prompting
- Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
- Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
- Stem: Rethinking Causal Information Flow in Sparse Attention
- StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning
- Step-Level Sparse Autoencoder for Reasoning Process Interpretation
- Step-resolved data attribution for looped transformers
- Step-Size Stability in Stochastic Optimization: A Theoretical Perspective
- STEP: Warm-Started Visuomotor Policies with Spatiotemporal Consistency Prediction
- STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
- StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning
- STLA: Spatiotemporal Lookahead Alignment for Post-Training Quantization
- Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization
- Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
- Stochastic Lifting for Generating Trajectories of Stochastic Physical Systems
- Stochastic Linear Bandits with Parameter Noise
- Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
- Stochastic Neural Ray Tracing for Radio Frequency Channel Modeling
- Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
- Stochastic Sparse Attention for Memory-Bound Inference
- Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding
- Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
- Stop When Further Reasoning Won’t Help: Attention-State Adaptive Generation in Reasoning Models
- StormInsight: Hierarchical Environmental Forcing and Vertical Coupling for Weather System Evolution
- STORM: Segment, Track, and Object Re-Localization from a Single Image
- Strategic Candidacy in Generative AI Arenas
- Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections
- Strategy-Aware Optimization Modeling with Reasoning LLMs
- Strategy Executability in Mathematical Reasoning: Leveraging Human–Model Differences for Effective Guidance
- Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
- Strat-Reasoner: Reinforcing Strategic Reasoning of LLMs in Multi-Agent Games
- StreamFlow: Theory, Algorithm, and Implementation for High-Efficiency Rectified Flow Generation
- Streaming Covariate Balancing via Discrepancy-Based Feature Coresets
- Streaming Sliced Optimal Transport
- Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage
- StretchTime: Adaptive Time Series Forecasting via Symplectic Attention
- STRIDE: Post-Training LLMs to Reason and Refine Bio-Sequences via Edit Trajectories
- Stronger Benchmarks for Prediction as a Service with Constraints
- Stronger Semantic Encoders Can Harm Relighting Performance: A Probe of Visual Priors via Augmented Latent Intrinsics
- StructMamPose: From Sequential Perception to Structural Reasoning for 3D Human Pose Estimation
- StructMAR: Structure-Aware Masked Autoregression for Explicit Layout Alignment in Text-to-Image Generation
- Structurally Aligned Subtask-Level Memory for Software Engineering Agents
- Structure Abstraction and Generalization in a Hippocampus-Entorhinal Inspired World Model
- Structure-Aware Consistency Priors for Shape from Polarization in Complex Media
- Structure-aware Granular-Ball based Information Bottleneck for Multi-modal Clustering
- Structure-Aware Riemannian Flow Matching for Registration and Fusion of Hyperspectral and Multispectral Images
- Structure-Centric Graph Foundation Model via Geometric Bases
- Structured 4D Latent World Model for Robot Planning
- Structured Data for Health
- Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges
- Structured Expert Routing with Multi-View Task Priors for Offline Meta-Reinforcement Learning
- Structured Multi-modal Graph Disentanglement for Psychiatric Diagnosis
- Structured Multi-step Jailbreaking under a Hamiltonian Generative Formulation
- Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
- Structure Enables Effective Self-Localization of Errors in LLMs
- Structure-Induced Information for Rerooting Levin Tree Search
- Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs
- ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
- STT-LLM: Structural-Temporal Tokenization for Adapting LLMs to Longitudinal Clinical Profiles
- Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs
- ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Grounding
- StyleDistillation: A New Insight of Image Style Enables Personalized Aesthetic Manipulation
- Subgroup Discovery with the Cox Model
- Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
- Submodular Optimization for Minimal Augmentation in Robust Language Model Alignment
- Subspace-Aware Feature Reshaping for Open-Set Graph Class-Incremental Learning
- SubspacePath Pruner: Inference-time Pruning via Probe-based Representation–Parameter Coupling
- Success-Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success
- SuCo: Sufficiency-guided Continuous Adaptive Reasoning
- Sufficiency is Relative: Evaluating LLM Explanations under Model-Induced Input Distributions
- SuperHype: Hypergraph Generation via Graph-Superposition Decomposition
- Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling
- Supervised Graph Contrastive Learning for Gene Regulatory Networks
- Supervised Guidance Training for Infinite-Dimensional Diffusion Models
- Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting
- Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
- Suppress and Diversify: Refining Robust Pathways for Corruption Robustness
- SURF: Separation via Unsupervised Remixing Flow
- Surgery: Mitigating Harmful Fine-Tuning for Large Language Models via Attention Sink
- SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
- SURGE:Unbiased Data Assimilation for Diffusion Model via Particle Filtering
- SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models
- SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
- SVD as a Fast Interpretability Method for Transformers
- SVL: Empowering Spiking Neural Networks for Efficient 3D Open-World Understanding
- SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
- SVRG and Beyond via Posterior Correction
- SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark
- SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
- SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
- SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
- SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
- SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?
- SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale
- SwiftPFN: Revisiting Row-Wise Attention–Only Tabular Foundation Models with Adaptive Early Exit
- Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression
- SWING: Unlocking Implicit Graph Representations for Graph Random Features
- SwitchCraft: Programmatic Design of State-Switching Proteins
- Swordsman: Entropy-Driven Adaptive Block Partition for Efficient Diffusion Language Models
- Sycophancy Towards Researchers Drives Performative Misalignment
- Symbal: Detecting Systematic Misalignments in Model-Generated Captions
- Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection
- Symbol-Equivariant Recurrent Reasoning Models
- Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning
- SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation
- Symmetries in language statistics shape the geometry of model representations
- Symmetries in PAC-Bayesian Learning
- Symmetry Reveals the In-Context Classifier: Transformers Implement Mean-Shift Dynamics
- SymSpectra: Symmetric Information Bottleneck Framework for Molecular Structure Recognition under Imbalanced Settings
- Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
- Synergistic Space-Vision Processing for Predicate Inference
- SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment
- SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
- SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
- Syntax vs. Semantics: How Transformers Learn Deep Dependencies
- Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
- Synthesizing Multimodal Geometry Datasets from Scratch and Enabling Visual Alignment via Plotting Code
- Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs
- T$^2$PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning
- T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation
- Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
- TabICooL: A better, faster, scalable, and open tabular foundation model
- TabMGP: Martingale Posterior with TabPFN
- TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
- TabularBERT: Binning-Based Self-Supervised Learning for Tabular Representation
- TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
- Tackling Fake Forgetting through Uncertainty Quantification
- Tackling Length Inflation Without Trade-offs: Group Relative Reward Rescaling for Reinforcement Learning
- TACTIC: Task-Aware Sparse Coordination Graphs for Multi-Task Multi-agent Reinforcement Learning
- TadABench-1M: A Large-Scale Wet-Lab Protein Benchmark For Rigorous OOD Evaluation
- TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment
- TAG: Tangential Amplifying Guidance for Hallucination-Resistant Sampling
- Tail Annealing for Heavy-Tailed Flow Matching
- Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
- Tailoring the Training: Difficulty-Aware Learning Strategy Allocation for Large Language Models
- Taking the GP Out of the Loop
- Talk, Judge, Cooperate: Gossip-Driven Indirect Reciprocity in Self-Interested LLM Agents
- Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework
- Taming Stochastic Gradient Descent: Almost Sure Convergence and Saddle-Point Avoidance under $(L_{0},L_{1})$-Smoothness
- Taming the Aleatoric Impulse in Off-Policy Reinforcement Learning
- Taming the Loss Landscape of PINNs with Noisy Feynman–Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
- Taming the Recent-Data Bias: Towards Robust Time Series Forecasting with Global Context
- TAMPO: Task- and Model-Aware Automatic Prompt Optimization for Robust and Controllable Auto-Routing in LLM-based Systems
- TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation
- TarGATE: Target-Aware Data Selection via Token-Attenuation Gates
- Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification
- Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space
- Target-Driven Policy Optimization for Sequential Counterfactual Outcome Control
- Target-Oriented Pretraining Data Selection via Neuron-Activated Graph
- TaRO: Temporal-Aware Reasoning Optimization for Video Temporal Grounding
- Task-and-Model-Aware Fractal-Consistency for Efficient LLM Reasoning
- Task-Aware Exploration via a Predictive Bisimulation Metric
- Task-Aware Mechanism: Hybrid MoE Vision Tower Towards Holistic Video Understanding
- Task-Awareness Improves LLM Generations and Uncertainty
- Task-Aware Preference Calibration for Direct Preference Optimization
- Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning
- Task-Driven Subspace Decomposition for Knowledge Sharing and Isolation in LoRA-based Continual Learning
- TaskLoom: Weaving Knowledge Across Tasks in World Models
- TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning
- TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
- TDM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward
- TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning
- Teaching Agents to Ask Effective Clarification Questions
- Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
- Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor
- TEAM: Temporal–Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
- TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination
- TeamWork: Multivariate Time Series Anomaly Detection via Asymmetric Role-aware Channel Modeling
- T-Edit: Triple-Branch Diffusion Anchoring for Consistent Editing
- TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers
- TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
- Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability
- Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance
- Tempora: Characterising the Time-Contingent Utility of Online Test-Time Adaptation
- Temporal-aware Flow Matching for Video Generation with Temporally Coherent Motion
- Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models
- Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
- Temporal Difference Learning for Diffusion Models
- Temporal-Emerged Prompting for Segment Anything in Multiframe Infrared Small Target Detection
- Temporal Preference Optimization for Unsupervised Retrieval
- Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
- Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future
- Temporal Straightening for Latent Planning
- Temporal Weighted Encoding: Towards Maximal-Capacity Spike Coding for ANN–SNN Conversion
- Terminal Dimension Reduction for Time Series with Applications
- TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation
- Testing For Distribution Shifts with Conditional Conformal Test Martingales
- Test-Time Anchoring for Discrete Diffusion Posterior Sampling
- Test-Time Debiasing with Probabilistic Prompts via Wasserstein Distance in Vision-Language Models
- Test-Time Detoxification without Training or Learning Anything
- Test-time Generalization for Physics through Neural Operator Splitting
- Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
- Test-Time Guidance for Flow-Based Generative Models via Parallel Tempering on Source Distributions
- Test-Time Learning of Causal Structure from Interventional Data
- Test-time Offline Reinforcement Learning on Goal-related Experience
- Test-Time Reinforcement Learning for Flow Matching
- Test-Time Training Is Secretly Linear Attention
- TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control
- TexEditor: Structure-Preserving Text-Driven texture Editing
- TextAtlas5M: A Large-Scale Dataset for Long Text Image Generation
- Text Before Vision: Staged Knowledge Injection Matters for Agentic RLVR in Ultra-High-Resolution Remote Sensing Understanding
- Text-Conditional JEPA for Learning Semantically Rich Visual Representations
- Text-Driven Fusion for Infrared and Visible Images: Achieving Image Scene Adaptation on Hyperbolic Space
- Text Generation as Continuous Latent Dynamics via Reinforcement Learning
- Text Has Curvature
- TextME: Bridging Unseen Modalities Through Text Descriptions
- TextMesh4D: Zero-shot Text-to-4D Mesh Generation
- TextResNet: Decoupling and Routing Optimization Signals in Compound AI Systems via Deep Residual Tuning
- Textual Stochastic Gradient Descent: Discrete Optimization of External Memory for Reasoning Language Agents
- Textual Supervision Enhances Geospatial Representations in Vision-Language Models
- TF-FACE: Time-Frequency Fusion Learning via Frequency-Domain Adaptive and Controllable Enhancement for Trajectory Prediction
- TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
- TFTF: Training-Free Targeted Flow for Conditional Sampling
- T-GINEE: A Tensor-Based Multi-Graph Representation Learning
- TGPO: Efficient Policy Optimization through Sequence Anchor and Information Gating
- TG-RAG: A Retrieval-Augmented Framework for Reasoning Guidance in Specialized Domains
- TGV-KV: Text-Grounded KV Eviction for Vision-Language Models
- The 2nd Workshop on Connecting Low-rank Representations in AI: From Practice to Theory
- The 2nd Workshop on Epistemic Intelligence in Machine Learning: Learning under Unknown Unknowns for Real-world Impact
- The Abstraction Gap in Vision-Language Causal Reasoning
- The Accumulation of Score Estimation Error in Diffusion Models
- The ACE Protocol: Operationalizing Language Model Activations for Better Calibration and Utility
- The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning
- The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models
- The benefits of full data shuffle, now with optimal I/O cost: $k$-wise independence and matrix transposition to the rescue
- The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works
- The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
- The Consistency Trap in LLMs: Generator-Evaluator Agreement and Vulnerability to Mistakes
- The Convergent Representation of Vision-Language Contrastive Learning: Geometry, Modality Gap and Shared Space Alignment
- The cost of commitment in option-based hierarchical RL
- The Cost of Information: Phase Transitions in Contextual Bandits with Paid Observations
- The Cost of Learning under Multiple Change Points
- The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents
- The Cylindrical Representation Hypothesis for Language Model Steering
- The data manifold under the microscope
- The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
- The Deterministic Horizon: When Extended Reasoning Fails and Tool Delegation Becomes Necessary
- The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?
- The Devil is in the Spectrum: Mitigating Representation Collapse in LLMs via Topologically Regularized Side-Path
- The Differences Between Direct Alignment Algorithms are a Blur
- The Double Dilemma in Multi-Task Radiology Report Generation: A Gradient Dynamics Analysis and Solution
- The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning
- The Efficiency Gap in Byte Modeling
- The Entropic Signature of Class Speciation in Diffusion Models
- The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
- The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought
- The Expressivity Limits of Transformers
- The Extra Tokens Matter: Disentangled Representation Learning with Vision Transformers
- The Fairness Hierarchy: A viewpoint from causal inference
- The First Drop of Ink: Nonlinear Impact of Misleading Information in Long-Context Reasoning
- The Fisher Dimension: Instance-Dependent Complexity for Causal Discovery
- The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models
- The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning
- The future of AI for biology at the intersection of generative and agentic AI
- The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
- The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-Modal Divergence
- The Geometric Origin of Grokking: Accelerating Generalization via Active Structural Reorganization
- The Geometric Reasoner: Manifold-Informed Latent Foresight Search for Long-Context Reasoning
- The Geometry of Narrow Fine-Tuning Degradation: Trajectory Lock-in and Spectral Bifurcation
- The Geometry of Projection Heads: Conditioning, Invariance, and Collapse
- The Geometry of Reasoning: Self-Evaluation via Layerwise Trajectory Evolution
- The Geometry of Representational Failures in Vision Language Models
- The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order
- The Geometry of Updates: Fisher Alignment at Vocabulary Scale
- The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
- The Hidden Risk: Membership Inference Attacks on Multimodal Federated Learning via Modality Imbalance
- The Hippocampal Place Field Gradient: A Bio-inspired Framework Building Multiscale Representation for Better Sample Efficiency
- The Ideal Expression Is Not a Local Optimum: A Revisit of EQL with Zero-Point Constraints
- The Illusion of Generalization: Instruction-Following, Task Bias and Contamination in Tabular Language Model Evaluation
- The impact of LoRA on Oversmoothing $\colon$ Understanding Catastrophic Forgetting in Mean-Field Attention Dynamics
- The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks
- The Implicit Bias of Depth: From Neural Collapse to Softmax Codes
- The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
- The Information Geometry of Softmax: Probing and Steering
- The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity
- The Invisible Lottery: How Subtle Cues Steer Algorithm Choice in LLM Code Generation
- The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting
- The Labyrinth and the Thread: Rethinking Regularizations in Sequential Knowledge Editing for Large Language Models
- The Latent Color Subspace: Emergent Order in High-Dimensional Chaos
- The Latent Guardian: Defending Collaborative Perception via Feature-Level Consistency Verification
- The Lie We Tell: Correcting the Euclidean Fallacy in Vision Language Action Policies via Score Matching on Tangent Space
- The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning
- The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
- The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
- The Optimal Sample Complexity of Linear Contracts
- The Optimal Token Baseline: Variance Reduction for Long-Horizon LLM-RL
- Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
- Theoretical Challenges in Learning for Branch-and-Cut
- Theoretical Characterization of Generalization in Knowledge Distillation
- Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
- Theoretical Investigation on Inductive Bias of Isolation Forest
- Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
- Theory of Continual Learning Against Data Poisoning Attacks
- Theory of Minimal Weight Perturbations in Deep Networks and its Applications for Low-Rank Activated Backdoor Attacks
- The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy
- The Pareto-optimal Trade-off between Regret and Statistical Inference in Linear Stochastic Bandits under Safety Constraints
- The Perception–Physics Paradox: Probing Scientific Alignment with TC-Atlas
- The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
- The Power of Power Law: Asymmetry Enables Compositional Reasoning
- The Quality-Utility Paradox: Why High-Reward Data Impairs Small Model Reasoning
- The Realignment Problem: When Right becomes Wrong in LLMs
- The Relative Instability of Model Comparison with Cross-validation
- The Role of Target Update Frequencies in Q-Learning
- The Safety-Aware Denoiser for Text Diffusion Models
- The Second Workshop on Agents in the Wild: Safety, Security, and Beyond
- The Second Workshop on the Impact of Memorization on Trustworthy Foundation Models
- The Secret Engine Behind RLHF: It's Contarstive Learning All Along
- The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs
- The Shape of Addition: Geometric Structures of Arithmetic in Large Language Models
- The Signal is in the Steps: Local Scoring for Reasoning Data Selection
- The Sign Estimator: Preference Modeling for LLM Alignment under Heterogeneity
- The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning
- The Stability of Singular Distribution: A Spectral Perspective on the Two-Phase Dynamics of Language Model Pre-training
- The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity
- The Surprising Difficulty of Search in Model-Based Reinforcement Learning
- The surprising strength of weak classifiers for validating neural posterior estimates
- ThetaEvolve: Test-time Learning on Open Problems
- THETA: Threshold-Based Exclusive Batching for Memory-Bandwidth-Constrained LLM Inference
- The Tell-Tale Norm: $\ell_2$ Magnitude as a Signal for Reasoning Dynamics in Large Language Models
- The Theory and Practice of MAP Inference over Non-Convex Constraints
- The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search
- The Truth Lies Somewhere in the Middle (of the Generated Tokens)
- The Truth Stays in the Family: Enhancing Contextual Truthfulness via Inherited Heads in Model Lineages
- The Two-Hump Problem: Bridging the Difficulty Gap in Mathematical Reinforcement Learning
- The Unlearnability Phenomenon in RLVR for Language Models
- The Value Function Semi-Algebraic Set in Partially Observable Markov Decision Processes
- The Value of Variance: Mitigating Debate Collapse in Multi-Agent Systems via Uncertainty-Driven Policy Optimization
- The Velocity Deficit: Initial Energy Injection for Flow Matching
- The Viscosity of Logic: Phase Transitions and Hysteresis in DPO Alignment
- Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models
- Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
- Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents
- Think in Cloud, Look at Edges: Semantic-Driven Query Decomposition for Efficient Video Reasoning
- Thinking in Flow: A Dissipative Stabilization Operator for Robust Autoregressive Reasoning
- Thinking in Latent Space: Progressive Multimodal Simplification for Visual Reasoning
- Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
- Thinking in Structures: Evaluating Spatial Intelligence through Reasoning on Constrained Manifolds
- Thinking with Geometry: Active Geometry Integration for Spatial Reasoning
- Think in Latent, Explain in Language: Self-Explainable Latent Reasoning
- Think Less, Act Early: Reinforced Latent Reasoning with Early Exit in Vision-Language-Action Models
- Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders
- Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction
- Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning
- Thinned Mean Field Langevin Dynamics
- This State Looks Like That: Self-Interpretable Reinforcement Learning Agents using Prototype Soft Actor-Critic
- Thoughtbubbles: an Unsupervised Method for Parallel Thinking in Latent Space
- ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
- ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
- Threat2Traffic: Multi-Agent Environment Synthesis for Malware Traffic Generation from Threat Intelligence
- Three Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data
- Threshold-Guided Optimization for Visual Generative Models
- Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG
- ThunderAgent: A Fast, Simple, and Program-Aware Agentic Inference System
- TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments
- Tightening the Score Matching Gap for Diffusion Models
- Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
- Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets
- Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning
- TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling
- TileSparse: Arithmetic-Intensity-Aware Sparse Attention for Compute-Bound LLM Decoding
- Tilt Matching for Scalable Sampling and Fine-Tuning
- Time-Conditioned Foreseeing: An EHR-Specific Foundation Model for Irregular Dynamics and Calendrical Time
- Time-Consistent Robust Multi-Objective Reinforcement Learning via a Bellman–Isaacs Weight-Adversary Recursion
- Time-CoT: Hierarchical Reasoning with Temporal Semantic Codes for Multivariate Time Series Classification
- TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
- TimeLAVA: Learning-Agnostic Valuation for Time Series Data
- TimeMRA: LLM-Empowered Time Series Forecasting via Multi-Scale Retrieval-Augmented Representations
- TimeOmni-VL: Unified Models for Time Series Understanding and Generation
- Time-PEFT: Temporal and Multichannel Complexity-Based Fine-Tuning for Time-Series Foundation Models
- TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance
- TimeSAE: Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
- TimeSeed: Effective Time Series Forecasting with Sparse Endogenous Variables
- Time-Series Decomposition as a standalone Task: A Mechanism-Driven Diagnostic Benchmark
- Time-series forecasting through the lens of dynamics
- Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
- Time series saliency maps: Explaining models across multiple domains
- Time Series, Vision, and Language: Exploring the Limits of Alignment in Contrastive Representation Spaces
- TimeSpot: Benchmarking Geo-Temporal Understanding in Vision–Language Models in Real-World Settings
- Timestep Rescheduling in Diffusion Inversion
- TIME: Tensor-Factorized Mixture-of-Experts with Intrinsic Routing for Lifelong Multimodal Knowledge Editing
- TiME: Test-Time Mixture-of-Experts Routing via Asymmetric CO-Optimal Transport for Continual Test-Time Adaptation
- TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
- TIMI: Training-Free Image-to-3D Multi-Instance Generation with Spatial Fidelity
- TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
- Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts
- TMD-Bench: A Multi-Level Evaluation Paradigm for Music–Dance Co-Generation
- T-measure: A Topology-Consistent Metric for Binary Segmentation
- TMS: Trajectory-Mixed Supervision for Reward-Free, On-Policy SFT
- TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions
- ToaSt: Token Channel Selection and Structured Pruning for Efficient ViT
- TodoEvolve: Learning to Architect Agent Planning Systems
- To Grok Grokking: Provable Grokking in Ridge Regression
- TokenDrop: Token-Level Importance-Aware Backward Propagation Skipping for Efficient LLM Fine-Tuning
- Token-Efficient Change Detection in LLM APIs
- Token-Free Hierarchical Indexing for RAG beyond LLM-based Summarization
- Tokenised Flow Matching for Hierarchical Simulation Based Inference
- Token-Level LLM Collaboration via FusionRoute
- TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
- Token Sample Complexity of Attention
- Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection
- Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning
- TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models
- TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior
- ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind
- TOM-SWE: User Mental Modeling For Software Engineering Agents
- ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
- TopAdapter: Topology-Aware Prompt Tuning for Efficient Point Cloud Understanding
- TopBench: A Benchmark for Implicit Prediction and Reasoning over Tabular Question Answering
- TopoDistill: Distilling Global System Topology for Causal Discovery in Multivariate Time Series
- Topological Active Inference for Task Disambiguation
- Topology-Aware Contrastive Learning: Regulating Representation Connectivity via Persistent Homology
- Topology-Preserving Neural Operator Learning via Hodge Decomposition
- Torus Graphs for Large Scale Neural Phase Analysis
- Toward Calibrated Mixture-of-Experts Under Distribution Shift
- Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning
- Toward Cybersecurity-Expert Small Language Models
- Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
- Toward Identifiable Sparse Autoencoders
- Toward More Reliable Agent Evaluation: A Component-Based Benchmark Auditing Pipeline
- Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
- Towards Achieving Optimal Strong Regret and Constraint Violation via Computational Efficient Model-free RL
- Toward Safe Quantization-Aware Fine-tuning: Understanding and Mitigating Safety Alignment Degradation
- Towards A Generative Protein Evolution Machine with DPLM-Evo
- Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
- Towards AI Agents In the Real World
- Towards a Science of AI Agent Reliability
- Towards Atoms of Large Language Models
- Towards a Unified Generative Model for Scarce Time Series with Domain Experts
- Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
- Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis
- Towards Complete Multi-Agent Coordination Policy Learning via Denoising Maximum Entropy Optimization
- Towards Completeness in Causal Discovery from Soft Interventions with Known Targets
- Towards Context-Invariant Safety Alignment for Large Language Models
- Towards Disentangled Preference Optimization Dynamics
- Towards Diverse Scientific Hypothesis Search with Large Language Models
- Towards Docking-oriented De Novo Ligand Design via Gradient Inversion
- Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background
- Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
- Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
- Towards Efficient LLMs Annealing with Principled Sample Selection
- Towards Execution-Grounded Automated AI Research
- Towards Fair Sequential Decision-Making: A Causal Decomposition Approach
- Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation
- Towards Fine-grained Robustness: Attention-guided Test-time Prompt Tuning for Vision-Language Models
- Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
- Towards Fully Parameter-Free Stochastic Optimization: Grid Search with Self-Bounding Analysis
- Towards Functional Correctness of Large Code Models with Selective Generation
- Towards Generalizable EEG-to-fMRI Synthesis via a Unified, Context-Aware Prompting Framework
- Towards Generative Graph Matching for Graph Edit Distance Computation
- Towards Hierarchy–Uniformity Equilibrium: Recovering Semantic Depth in Hypergraph Contrastive Learning
- Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding
- Towards Long-Horizon Interpretability: Efficient and Faithful Multi-Token Attribution for Reasoning LLMs
- Towards Multimodal Large Language Models with Both Training and Inference Efficiency
- Towards One-for-All Anomaly Detection for Tabular Data
- Towards One-to-Many Temporal Grounding
- Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training
- Towards Optimal Robustness in Learning-Augmented Paging
- Towards Parameter-Free Temporal Difference Learning
- Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization
- Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
- Towards Professional-Grade Financial Agents: Benchmarking, Tooling, and Structured Reasoning
- Towards Realistic Lifelong Re-identification: Identity Recurrence with Changing Clothes
- Towards Reliable Marking and Verification of AI-Generated Text via Geometry-aware Sentence-level Watermarking
- Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
- Towards Robust Human-AI Complementarity under Uncertainty
- Towards Rule-Based Knowledge Sharing in Federated Learning
- Towards Scalable and Consistent 3D Editing
- Towards Seed-Robust Safety Alignment in Text-to-Image Models
- Towards Solving the Gilbert-Pollak Conjecture via Large Language Models
- Towards Spectroscopy: Susceptibility Clusters in Language Models
- Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions
- Towards Sub-second Biological Foundation Model Infrastructure: A Quantized Consistency Diffusion Framework for Molecular Docking
- Toward Stable Value Alignment: Introducing Independent Modules for Consistent Value Guidance
- Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
- Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression
- Towards the Training of Deeper Predictive Coding Neural Networks
- Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
- Towards Trustworthy and Identifiable Virtual Face Generation
- Towards Trustworthy Video Anomaly Understanding: A Class-Guided Chain-of-Evaluation Metric and An Anomaly-focused Meta-Benchmark
- Toward Subspace-Perturbed Trajectory-Aware Backdoor Attacks in Deep Reinforcement Learning
- Towards Understanding Adam Convergence on Highly Degenerate Polynomials
- Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm
- Towards Understanding Generalization of Federated Adversarial Learning: Perspective of Algorithmic Stability
- Towards Understanding Massive Activations in Attention Sink Mechanism
- Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition
- Towards Understanding Steering Strength
- Towards Understanding the Dynamics of Low-Rank Adaptation
- Towards Unified Multimodal Pretraining
- Towards Uniformity and Alignment for Multimodal Representation Learning
- Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
- Towards Whole-corpus Reconstruction of Heterogeneous RAG Knowledge Bases
- Toward Training Superintelligent Software Agents through Self-Play SWE-RL
- Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Autonomous Machine Learning Engineering
- Toward Understanding Adversarial Distillation: Why Robust Teachers Fail
- TPGDiff : Hierarchical Triple-Prior Guided Diffusion for Image Restoration
- T-POP: Test-Time Personalization with Online Preference Feedback
- TPV: Parameter Perturbations Through the Lens of Test Prediction Variance
- TQL: Scaling Q-Functions with Transformers by Preventing Attention Collapse
- TraceRouter: Robust Safety for Large Foundation Models via Path-Level Intervention
- TRACER: Persistent Regularization for Robust Multimodal Finetuning
- TRACER: Trajectory Risk Aggregation for Critical Episodes in Agentic Reasoning
- TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels
- TRACE: Toulmin-based Reasoning Assessment through Constructive Elements for LLM CoT Evaluation
- TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
- Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection
- Tracing the Emergence of Symbol Grounding in Multimodal Language Models
- Tracing the Persona Circuit: How Large Language Models Encode and Express Character Traits
- Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
- Tractable Expected Information Gains for Exponential Family Posteriors
- Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing
- Trainable Nonexpansive Denoisers for Contractive Image Reconstruction
- Train for Truth, Keep the Skills: Binary Retrieval-Augmented Reward Mitigates Hallucinations
- Training AI Co-Scientists Using Rubric Rewards
- Training Data Efficiency in Multimodal Process Reward Models
- Training Deep Spiking Neural Networks without Normalization
- Training Diffusion Language Models for Black-Box Optimization
- Training-Free Adaptation of Diffusion Models via Doob's $h$-Transform
- Training-Free Adversarial Robustness in Deep Learning MRI Reconstruction
- Training-Free Bayesian Filtering with Generative Emulators
- Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
- Training-Free Coverless Multi-Image Steganography with Access Control
- Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
- Training-Free Guided Diffusion for Planning: A Unified Framework via Doob’s h-Transform with Safety Guarantees
- Training-Free Hashing-Based Attention via Binary Principal Components
- Training-Free Hierarchical Working Memory for Small Language Model Agents
- Training-Free Multimodal Large Language Model Orchestration
- Training-Free Rate-Distortion-Perception Traversal With Diffusion
- Training-Free Sparse Attention for Fast Video Generation via Offline Layer-Wise Sparsity Profiling and Online Bidirectional Co-Clustering
- Training–Inference Consistent Segmented Execution for Long-Context LLMs
- Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
- Training LLM Agents to Empower Humans
- Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
- Training-Trajectory-Aware Token Selection
- Training with Honeypots: Reshaping How LLMs Fail
- Train Once, Reuse Everywhere: Generalizable Implicit ICL by Routing Attention
- Trajectory-Aware Certified Decentralized Unlearning via SGD Stability
- Trajectory-Aware Heuristic Learning for Combinatorial Search
- Trajectory-Aware Spiking DiTs Conversion via Membrane Potential Error-Feedback
- Trajectory Consistency for One-Step Generation on Euler Mean Flows
- Trajectory-Level Data Augmentation for Offline Reinforcement Learning
- Trajectory-Level Speculative Decoding for Diffusion Language Models
- Trajectory Seriation via Spectral Tangent Alignment and Global Embedding
- Trajectory-Stabilized Inference for Diffusion-Based Video Inpainting
- Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
- Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment
- Transfer Learning in High-dimensional Ising Models
- Transfer Learning in Nonparametric Regression with Deep ReLU Networks
- Transformed Latent Variable Multi-Output Gaussian Processes
- Transformer Circuits Can Realize Clustering Algorithms
- Transformers Can Learn Posterior Predictive Distributions In-Context
- Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
- Transformers learn factored representations
- Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs
- Transformers Provably Learn Algorithmic Solutions for Graph Connectivity, But Only with the Right Data
- Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently
- Transforming Weather Data from Pixel to Latent Space
- Transform Trained Transformer for Accelerating Native 4K Video Generation
- Transitive Representation Learning Enhances Histopathology Annotation
- Transitivity Meets Cyclicity: Explicit Preference Decomposition for Dynamic Large Language Model Alignment
- TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
- TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation
- Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
- Transport and Merge: Cross-Architecture Merging for Large Language Models
- Transport Clustering: Solving Low-Rank Optimal Transport via Clustering
- Transporting Task Vectors across Different Architectures without Training
- Transport or Discard: Robust Unbalanced Optimal Transport for Cross-Domain Policy Adaptation
- TranX-Adapter: Bridging Artifacts and Semantics within MLLMs for Robust AI-generated Image Detection
- TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches
- Treatment Responder Classification with Abstention
- TreeCUA: Efficiently Scaling GUI Automation with Tree-Structured Verifiable Evolution
- TreePO: Enhancing Policy Efficacy and Inference Efficiency with Tree Modeling
- Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
- Tree-Structured Orthonormal Decomposition of the Aitchison Simplex
- Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
- TriAttention: Efficient Long Reasoning with Trigonometric KV Compression
- TriForces: Augmenting Atomistic GNNs for Transferable Representations
- Trifuse: Enhancing Attention-Based GUI Grounding via Multimodal Fusion
- TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
- TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios
- Tri-Scale Neural ODEs for Continuous Multi-Omics Disease Modeling
- TritonGym: A Benchmark for Agentic LLM Workflows in Triton GPU Code Generation
- Trojan-Speak: Bypassing Constitutional Classifiers with No Jailbreak Tax via Adversarial Finetuning
- Trust3R: Unifying Feed-Forward Pointmap Prediction and Evidential Learning for Trust-Aware 3D Reconstruction
- Trust Functions: Near Lossless Weak-to-Strong Generalization by Learning to Trust the Weak Teacher
- Trust-Region Diffusion Policies for Massively Parallel On-Policy RL
- Trust Region Inverse Reinforcement Learning
- Trust Region Masking for Long-Horizon LLM Reinforcement Learning
- Trustworthy AI for Good Workshop
- Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
- TrustworthyQENN: A Quantum Evidential Neural Network Based on Complex-Valued Contrastive Learning for Uncertainty Pattern Classification
- Truthfulness Does Not Scale Like Reasoning: Why Polling Fails as a Proxy Verifier
- TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
- TSFAdv: Frequency-Guided Black-Box Adversarial Attacks on Time Series Forecasting
- TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
- TSMGen: Target-Specific Molecule Generation via Higher-Order Structural Dependencies and Context-Aware Bidirectional Fusion
- TSP with predictions
- TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models
- TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
- Tucker Attention: A generalization of approximate attention mechanisms
- TuneAhead: Predicting Fine-tuning Performance Before Training Begins
- Tuning-Free One-Class Discriminant Learning for Tabular Anomaly Detection
- Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity
- Turbo4DGen: Ultra-Fast Acceleration for 4D Generation
- Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers
- TurboGS: Accelerating 3D Gaussian Splatting via Error-Guided Sparse Pixel Sampling and Optimization
- TUR-DPO: Topology- and Uncertainty-Aware Direct Preference Optimization
- Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation
- Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning
- Turning Bias into Bugs: Bandit-Guided Style Manipulation Attacks on LLM Judges
- Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Multi-Stream Environments
- Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization
- Tvcache: A Tool-Value Cache for Post-Training LLM Agents
- TVDRNet: Text-driven Viewpoint Optimization via Differentiable Rendering for 3D Reasoning Segmentation
- TVI-CoT: Text-Visual Interleaved Chain-of-Thought Reasoning for Multimodal Understanding
- Twice Sequential Monte Carlo for Tree Search
- TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization
- Twins: Learn to Predict Unified Representations with Focal Loss
- TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins
- TWLA: Breaking the Barrier to W1.58A4 Post-Training Quantization for LLMs
- Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
- Two-dimensional quantization for geometry-aware audio coding
- Two-Layer Linear Auto-Regressive Models Estimate Latent States
- Two Modalities Are Better Than One: Efficient Adversarial Purification via Multimodal Diffusion Models
- Two-Parameter Flows for Learning Population Dynamics of Physical Systems
- Two-Stage Unit Tying for Simplifying Differentiable Logic Gate Networks
- U$^3$CF: Unbiased, Unconfounding, and Unified Causal Framework for Multi-Target Domain Adaptation
- UAV$^2$: A Unified and Adaptive Scheduling Framework for UAV Autopilot System with Reinforcement Learning
- Ubiquity of Homeostatic Hebbian Dynamics in Regularized Learning
- UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
- U-Cast: A Surprisingly Simple Frontier Probabilistic AI Weather Forecaster
- UCPO: Uncertainty-Aware Policy Optimization
- UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
- UFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation
- UGround: Towards Unified Visual Grounding with Unrolled Transformers
- UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing
- UI2Code^N: UI-to-Code Generation as Interactive Visual Optimization
- Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov–Arnold Networks
- UltraHorizon: Benchmarking LLM-Agent Capabilities in Ultra Long-Horizon Scenarios
- UltraLIF: Fully Differentiable Spiking Neural Networks via Ultradiscretization and Max-Plus Algebra
- UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
- Unbiased Alignment for Large Language Models with Noisy Preferences
- Unbiased and Second-Order-Free Training for High-Dimensional PDEs
- Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization
- Unbiased Principles, Robust Rewards
- Unbiased Reward Modeling from Implicit Preference
- Uncertainty-Aware Clarification in LLM Agents with Information Gain
- Uncertainty-Constrained Trustworthiness for Graph Learning
- Uncertainty-Guided Exploration and Stable Planning for Sparse-Reward Manipulation from Limited Demonstrations
- Uncovering Bias Mechanisms in Observational Studies
- Uncovering Competency Gaps in Large Language Models and Their Benchmarks
- Uncovering Grounding IDs: How External Cues Shape Multi-Modal Binding
- Uncovering Hidden Triggers: Backdoor Attribution in Language Models
- Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing
- Uncovering the Gradient Geometry of Long CoT: A Spectral-guided Approach to Reasoning Distillation
- Uncovering the Latent Potential of Deep Intermediate Representations
- Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
- Understanding and Mitigating Token-Pruning-Induced Vulnerabilities in VLMs
- Understanding Behavior Cloning with Action Quantization
- Understanding Data Temporality Impact on Large Language Models Pre-training
- Understanding Dynamic Compute Allocation in Recurrent Transformers
- Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach
- Understanding Generalization and Forgetting in In-Context Continual Learning
- Understanding Generalization from Embedding Dimension and Distributional Convergence
- Understanding LoRA as Knowledge Memory: An Empirical Analysis
- Understanding MARS: When Scaling Momentum Provably Helps
- Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective
- Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
- Understanding Private Learning From Feature Perspective
- Understanding Reasoning Collapse in LLM Agent Reinforcement Learning
- Understanding SAM through Minimax Perspective
- Understanding Self-Supervised Learning via Latent Distribution Matching
- Understanding the Ability of LLMs to Handle Character-Level Perturbation
- Understanding the Gaps in Satisficing Bandits
- Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO
- Understanding Transfer Learning of RNA Foundation Models on Downstream Tasks
- Understanding Truncated Positional Encodings for Graph Neural Networks
- Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation
- Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
- UnHype: CLIP-Guided Hypernetworks for Dynamic LoRA Unlearning
- UniCode: Augmenting Evaluation for Code Reasoning
- UniCoD: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning
- Uni-DocRobust: Universal Plug-and-Play Robustness Enhancement for Multi-modal LLMs via Feature Restoration
- UniDrag: Unified Multi-Field Prediction and Robust Shape Optimization for Vehicle Aerodynamics
- UniFast-HGR: Scalable and Efficient Maximal Correlation for Multimodal Models
- Unified Episodic and Semantic Memory via Modulating Transformer FeedForward Layers
- Unified Multimodal Autoregressive Modeling with Shared Context—Visual Tokenizer is Key to Unification
- Unified Multimodal Visual Tracking with Dual Mixture-of-Experts
- Unified Safe In-context Image Generation in Multimodal Diffusion Transformers
- Unified Time Series Explanations via Semi-Amortized Optimization and Instance-level Multi-Expert Knowledge Distillation
- UniFLoW: Universal Multi-Modal Federated LoRA Fine-Tuning Framework with Analytical Aggregation
- Unifying Adversarial Robustness and Training Across Text Scoring Models
- Unifying and Optimizing Data Values for Selection via Sequential Decision-Making
- Unifying Deep Stochastic Processes for Image Enhancement
- Unifying Heterogeneous Degradations: Uncertainty-Aware Diffusion Bridge Model for All-in-One Image Restoration
- Unifying Heterogeneous Multi-Modal Remote Sensing Detection Via Language-Pivoted Pretraining
- Unifying Low Dimensional Spectra in Deep Learning
- Unifying Masked Diffusion Models with Various Generation Orders and Beyond
- Unifying Stacking and Cascading for Efficient Ensemble Inference
- Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets
- UniMapping: Unified SLAM Framework for Map-Centric Embodied Perception
- UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis
- UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
- UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms
- UniRTL: Unifying Code and Graph for Robust RTL Representation Learning
- UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling
- Unison: Benchmarking Unified Multimodal Models via Synergistic Understanding and Generation
- UniSparse: Combining Weight Pruning and Spike Sparsification in Spiking Neural Networks
- UniSVQ: 2-bit Unified Scalar-Vector Quantization
- Unitary Convolutions for Message-passing and Positional Encodings on Directed Graphs
- Universal Algorithm-Implicit Learning
- Universal Approximation with Softmax Attention
- Universality, Function Composition, and Algorithm Emulation All In-Context
- Universal Learning of Nonlinear Dynamics
- Universal Multiclass Transductive Online Learning
- Universal One-third Time Scaling in Learning Peaked Distributions
- Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs
- Universal Redundancies in Time Series Foundation Models
- UNIVERSAL REPRESENTATION OF GENERALIZED CONVEX FUNCTIONS AND THEIR GRADIENTS
- Universal Skeleton Understanding via Differentiable Rendering and MLLMs
- Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
- Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
- Unlearning Isn’t Forgetting: Revealing Hidden Leakage in Class Unlearning Evaluations
- Unlearning’s Blind Spots: Over‑Unlearning and Prototypical Relearning Attack
- Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
- Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
- Unleashing the Representational Power of Fourier Shapes for Attacking Infrared Object Detection
- Unlocking Cross-Modal Biosignal Synthesis: A Temporally-Aware VAE-Diffusion Model
- Unlocking Noise-Resistant Vision: Key Architectural Secrets for Robust Models Against Gaussian Noise
- Unlocking Speech–Text Compositional Powers: Instruction-Following Speech Language Models without Instruction Tuning
- Unlocking the Potential of Continual Model Merging: An ODE Perspective
- Unlocking Zero-Shot Geospatial Reasoning via Indirect Rewards
- UnMaskFork: Test-Time Scaling for Masked Diffusion via Deterministic Action Branching
- Unpaired Visual Editing with Self-Consistent Flow Matching
- Unraveling Syntax: Language Modeling and the Substructure of Grammars
- Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents
- Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
- Unsupervised Camouflaged Object Detection with Dual-Eigenvector Spectral Pseudo-Labeling and Contrastive Refinement
- Unsupervised Diffusion for Combinatorial Optimization via Adjoint Matching
- Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
- Unsupervised Hierarchical Skill Discovery
- Unsupervised Mode Discovery for Fine-tuning Multimodal Generative Policies
- Unsupervised Neural Langevin Sampler for Mixed Integer Linear Programming
- Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
- Unsupervised Process-Aware Coreset Selection for In-Context Learning
- Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking
- Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization
- Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
- Unveiling Prior-data Fitted Networks on Causal Effect Estimation: Pre-training or Finetuning?
- Unveiling the Entropy Dynamics of Chain-of-Thought Reasoning
- Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
- Unveiling the Role of Data Uncertainty in Tabular Deep Learning
- Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
- Unveiling the Visual Counting Bottleneck in Vision-Language Models
- UOTIP: Unbalanced Optimal Transport Map for Unpaired Inverse Problems
- Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
- UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations
- UrbanMLLM: Joint Learning of Cross-view Imagery for Urban Understanding
- URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization
- USE : A Unified Self-Ensembling Framework for Test-Time Prompt Tuning
- User-Aware Active Knowledge Acquisition for Emotional Support Dialogue
- Use What You Know: Causal Foundation Models with Partial Graphs
- Utility Boundary of Dataset Distillation: Scaling and Coverage Laws
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
- Utonia: Toward One Encoder for All Point Clouds
- V1: Unifying Generation and Self-Verification for Parallel Reasoners
- V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning
- Value Aggregation with Uncertainty in Online Decentralized MARL
- Value-as-Return: A Two-Stage Framework to Align on the Optimal Score Function
- VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models
- VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation
- Variable Clustering via Distributionally Robust Nodewise Regression
- Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
- Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments
- Variance-Reduced $(\varepsilon, \delta)-$Unlearning using Forget Set Gradients
- Variance-Reduced Zeroth-Order Langevin Dynamics for Non-Log-Concave Black-Box Sampling and Inverse Problems
- Variational Adapter for Cross-modal Similarity Representation
- Variational Bayesian Flow Network for Graph Generation
- Variational Entropic Optimal Transport
- Variational Flow Maps: Make Some Noise for One-Step Conditional Generation
- Variational Inference for Uncertain Optimal Transport via Sinkhorn Parametrization
- Variational inference via Gaussian interacting particles in the Bures-Wasserstein geometry
- Variational Learning for Insertion-based Generation
- Variational Learning of Disentangled Representations
- Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
- Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance
- Var-JEPA: Variational Joint-Embedding Predictive Architecture – Bridging Predictive and Generative Self-Supervised Learning
- VBA: Vector Bundle Attention for Intrinsically Geometric Representation Learning
- VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing
- VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
- VecDesigner: Exploring Visual Guidance and Structural Consistency for Semantic Typography
- VecMol: Vector-Field Representations for 3D Molecule Generation
- Vector Linking via Cross-Model Local Isometric Consistency
- Vector Quantization using Gaussian Variational Autoencoder
- VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphs
- Veda: Scalable Video Diffusion via Distilled Sparse Attention
- Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention
- VELR: Efficient Video Reward Feedback via Ensemble Latent Reward Models
- VENOMREC: Cross-Modal Interactive Poisoning for Targeted Promotion in Multimodal LLM Recommender Systems
- VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics
- VEQ: Modality-Adaptive Quantization for MoE Vision-Language Models
- VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models
- Verbalized Bayesian Persuasion
- Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
- Verifiable Multimodal Reasoning: Fact-level Attribution with Multimodal Sources
- Verifying Meta-Awareness via Predictive Rewards in Reasoning Models
- VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification
- VeRO: An Evaluation Harness for Agents to Optimize Agents
- Very Efficient Listwise Multimodal Reranking for Long Documents
- “very likely” Means “uncertain”? How LLMs Diverge from Humans in Linguistic Uncertainty Quantification
- VFMF: Dense Forecasting by Generating Foundation Model Features
- VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency
- VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
- Vibe Checker: Aligning Code Evaluation with Human Preference
- VIBE: Disentangling Social Dynamics via Kinematics-Informed Variational Inference for Behavioral Emotion
- Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
- Video-Based Optimal Transport for Feedback-Efficient Offline Preference-Based Reinforcement Learning
- Video-BCI: Bayesian Cognitive Integration of Self-Prior Hypotheses for Video Understanding
- VideoBrain: Learning Adaptive Frame Sampling for Long Video Understanding
- VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization
- Video-in-the-Loop: Span-Grounded Long Video QA with Interleaved Reasoning
- VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
- VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding
- Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding
- Video-OPD: Efficient Post-Training of Multimodal Large Language Models for Temporal Video Grounding via On-Policy Distillation
- video-SALMONN S: Memory-Enhanced Streaming Audio-Visual LLM
- VideoSEAL: Separating Planning from Answer Authority for Agentic Long Video Understanding
- VideoSeeker: Native Interleaved Clue Seeking for Long Video Multi-Hop Reasoning
- VideoSEG-O3: A Multi-turn Reinforcement Learning Framework for Reasoning Video Object Segmentation
- Video-SVD: Efficient Video Diffusion via Orthogonal Basis Composition
- VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
- VideoTrace-R1: Long Video-based Retrieval-Augmented Generation via Temporal Path Graph Understanding
- VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning
- VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understanding
- ViEEG: Hierarchical Visual Neural Representation for EEG Brain Decoding
- ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Diffusion Models
- View Space: Learning Representation across Arbitrary Graphs
- VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph
- VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning
- VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference
- VIRUS: Injecting Persistent Cognitive Pathogens into Stateful Zero-Shot Object Navigation Agents
- Vision-aligned Latent Reasoning for Multi-Modal Large Language Model
- Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models
- Vision in One Vector: Implicit Visual Compression with Diffusion Foundation Models
- Vision-Language-Action Pretraining from Large-Scale Human Videos
- Vision Language Models Cannot Reason About Physical Transformation
- VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning
- Vision Transformer Finetuning Benefits from Non-Smooth Components
- VisionWebDev: A Hierarchical Benchmark for Visual Website Development with Agent Verification
- Visual Implicit Autoregressive Modeling
- Visual Para-Thinker: Divide-and-Conquer Reasoning for Visual Comprehension
- Visual Persuasion: What Influences Decisions of Vision-Language Models?
- VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge
- VisualScore: Learning Holistic Visual Quality Scores via Multi-Task Reasoning
- ViSurf: Visual Supervised-and-Reinforcement Fine-Tuning for Large Vision-and-Language Models
- ViTok-v2: Scaling Native-Resolution Autoencoders to 5B
- VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
- VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Models
- VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
- VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model
- VLANeXt: Recipes for Building Strong VLA Models
- VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
- VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models
- VlogReward: Learning Multi-Dimensional Evaluation for Vlog Editing
- VocSim A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio
- Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction
- VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection
- VR-Thinker: Boosting Multimodal Reward Models through Think with Image Reasoning
- VSCD: Video-based Scene Change Detection in Unaligned Scenes
- VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning
- Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
- Wait, Wait, Wait... Why Do Reasoning Models Loop?
- Walrus: A Cross-domain Foundation Model for Continuum Dynamics
- WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving
- Wasserstein Geometry-Aware Adaptive Control via Meta-Learning
- WatchLog: Efficient and Interpretable Event Reasoning for Endpoint Detection and Response Logs with Multimodal LLMs
- Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
- Watermarking Graph Neural Networks via Explanations for Ownership Protection
- Watermarking LLM Agent Trajectories
- WaterSIC: information-theoretically (near) optimal linear layer quantization
- WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention
- WAVE: Window-Aware Vocabulary-Efficient Early-Exit for Training-Free LLM Acceleration
- WBMM: Windowed Batch Matrix Multiplication for Efficient Large Receptive Field Convolution
- Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
- Weakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation
- Weak-to-Strong Generalization via Bregman Bias–Variance Decomposition
- Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection
- WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation
- Weaving Graph over Tokens: Contextualizing Structured Sequences for LLMs
- Weaving in the Clouds: Achieving Synergistic Collaboration among LLM Agents via Federated Learning
- WebWorld: A Large-Scale World Model for Web Agent Training
- WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
- Weight Decay Improves Language Model Plasticity
- Weight-Space Learning for Certifiable Few-shot Transfer Learning
- Weight-sparse transformers have interpretable circuits
- Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer
- Weight Updates as Activation Shifts: A Principled Framework for Steering
- Welfare-Optimal Classification with Accuracy Auctions
- Well-Posed KL-Regularized Control via Wasserstein and Kalman–Wasserstein KL Divergences
- WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
- WET: Mitigating World-Conditioned Knowledge Conflicts via World Entropy Tethering
- WEVSR: Adapting Video Diffusion Generators to Real-World Video Super‑Resolution with Wavelet-Enhanced VAE Encoder
- WF-Bench: A Benchmark for Neural-Network WaveFunction Expressivity and Scaling Laws
- WFR-MFM: One-Step Inference for Dynamic Unbalanced OT
- What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
- What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
- What Does Flow-Matching Bring to TD-Learning?
- What Does Preference Learning Recover from Pairwise Comparison Data?
- What Does Thompson Sampling Optimize?
- What Does Vision Tool-Use Reinforcement Learning Really Learn? Disentangling Tool-Induced and Intrinsic Effects for Crop-and-Zoom
- What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
- What If We Allocate Test-Time Compute Adaptively?
- What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
- What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
- What is Missing? Explaining Neurons Activated by Absent Concepts
- What Language is This? Ask Your Tokenizer.
- What Linear Probes Miss: Multi-View Probing for Weight-Space Learning
- What Makes a Desired Graph for Relational Deep Learning?
- What Makes a Good Representation for Single-Cell Perturbation Prediction?
- What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression
- What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis
- What Makes Synthetic Data Effective in Image Segmentation
- What Makes Value Learning Efficient in Residual Reinforcement Learning?
- What Preferences Can—and Cannot—Predict in Multi-Agent Online Learning
- What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
- What Reward Structure Enables Efficient Sparse-Reward RL? A Proof-of-Concept with Policy-Aware Matrix Completion
- What will be left for us to work on?
- What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity
- When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
- When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems
- When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets
- When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
- When and How Human Curation Backfires: Preference Alignment under Multi-Model Self-Consuming Loop
- When Attributes Disagree: Gradient Conflict in Image Aesthetic Assessment
- When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents
- When Can We Trust Survival Model Evaluation ?
- When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs
- When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
- When Diffusion Language Models Hesitate: Detecting and Correcting Visual Hallucinations via Confidence Fluctuation
- When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
- When Do Diffusion Models learn to Generate Multiple Objects?
- When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Control
- When does predictive inverse dynamics outperform behavior cloning?
- When Does Sparsity Mitigate the Curse of Depth in LLMs
- When Do Graph Foundation Models Transfer? A Data-Centric Theory
- When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression
- When Drafts Evolve: Speculative Decoding Meets Online Learning
- When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems
- When Generalized Zero-Shot Learning Meets PU Learning: A Plug-and-Play Framework for Seen-Class Bias Mitigation
- When Is Rank-1 Enough? Geometry-Guided Initialization for Parameter-Efficient Fine-Tuning
- When Is Symbolic Regression Tractable?
- When Iteration Helps and Hurts in Self-Training: Denoising vs. Signal Forgetting
- When Labelers Stay Silent: The Power of Ties in Cost-Effective Preference Learning
- When less gives more: bias from small dataset can speed up training
- When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
- When LLMs Encounter Open-world Graph Learning: A Fresh View on Unlabeled Data Uncertainty
- When Model Merging Breaks Routing: Training-Free Calibration for MoE
- When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
- When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
- When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
- When Preference Labels Fall Short: Aligning Diffusion Models from Real Data
- When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs
- When Random Saliency Looks Trained: Architectural Center Bias in CNN Interpretability
- When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents
- When RL Meets Adaptive Speculative Training: A Unified Training-Serving System
- When Sample Selection Bias Precipitates Model Collapse
- When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models
- When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging
- When Simple Problems Wear Complex Costumes: Improving Efficiency in LRM’s Adaptive Reasoning
- When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs
- When Softmax Fails at the Top: Extreme‑Value Corrections for InfoNCE
- When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
- When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models
- When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
- When to Think, When to Speak: Learning Disclosure Policies for Large Language Model Reasoning
- When to Trust the Cheap Check: Weak and Strong Verification for Reasoning
- Where Concept Erasure Should Occur: Concept–Layer Alignment in Text-to-Video Diffusion Models
- Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
- Where Rectified Flows Leak: Characterizing Membership Signals Along the Interpolation Path
- Where Signals Are Sparse, We Synthesize: Reinforcing Self-Corrective Reasoning in Vision–Language Models via Rollout Augmentation
- Which Algorithms Can Graph Neural Networks Learn?
- Which Heads Matter for Reasoning? RL-Guided KV Cache Compression
- Which LLM Multi-Agent Protocol to Choose?
- Which Reasoning Traces Are Worth Generating Further? Data Curation for Training Reasoning Models
- WhisperSplat: Lossless Steganography in 3D Gaussian Splatting
- Who can we trust? LLM-as-a-jury for Comparative Assessment
- Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
- Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems
- Who Said Neural Networks Aren't Linear?
- Who’s in Charge? Disempowerment Patterns in Real-World LLM Usage
- Who Transfers Safety? Identifying and Targeting Cross-Lingual Shared Safety Neurons
- Why Agentic Theorem Prover Works: A Statistical Provability Theory of Mathematical Reasoning Models
- Why Are Linear RNNs More Parallelizable?
- Why DDIM Hallucinates More than DDPM: A Theoretical Analysis of Reverse Dynamics
- Why Dedicated Critics: Eliminating Target Drift in Multi-Constraint RL
- Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
- Why Do We Need Warm-up? A Theoretical Perspective
- Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
- Why ReLU? A Bit-Model Dichotomy for Deep Network Training
- Why Specialist Models Still Matter: A Heterogeneous Multi-Agent Paradigm for Medical Artificial Intelligence
- Why Tree-Style Branching Matters for Thought Advantage Estimation in GRPO
- Width Independent Bounds for the Local Lipschitz Constant of Deep Neural Networks at Random Initialization and after Lazy Training
- WildActor: Unconstrained Identity-Preserving Video Generation
- WildCat: Near-Linear Attention in Theory and Practice
- WinDeskGround: A Benchmark for Robust GUI Grounding in Complex Multi-Window Desktop Environments
- WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling
- Winformer: Transcending Pairwise Similarity for Time-series Generation
- WinQ: Accelerating Quantization-Aware Training of Large Language Models around Saddle Points
- WISE: World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
- With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots
- WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models
- Words Towards Explainability: Caption Label-Free Learning via Dual Loop Agentic Time Series Captioning
- Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
- Workshop on Human-AI Co-Creativity: Advances, Opportunities, and Challenges
- Workshop on Mechanistic Interpretability
- Workshop on Weight-Space Symmetries: from Foundations to Practical Applications
- WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching
- WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
- WorldCompass: Reinforcement Learning for Long-Horizon World Models
- World Guidance: World Modeling in Condition Space for Action Generation
- WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting
- World-Model Inspired Emotion-aware Token Refinement for Training-Free Multimodal Emotion Recognition
- WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
- World-R1: Reinforcing 3D Constraints for Text-to-Video Generation
- World-Shaper: A Unified Framework for 360° Panoramic Editing
- WorldTravel: A Realistic Multimodal Travel-Planning Benchmark with Tightly Coupled Constraints
- WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning
- WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
- XDomainBench: Diagnosing Reasoning Collapse in High-Dimensional Scientific Knowledge Composition
- X-EviProbe: Post-hoc Parameter-free Evidential Uncertainty Quantification for Frozen Graph Neural Networks
- xKV: Cross-Layer KV-Cache Compression via Aligned Singular Vector Extraction
- xLSTM Distillation: Achieving Teacher-Student Parity Through Efficient Hybrid Architectures
- X-MoGe: A Cross-Modal Adaptation Framework with Mixture-of-Experts and Geometry Guidance for Heterogeneous Collaborative Perception
- XPERT: Expert Knowledge Transfer for Effective Training of Language Models
- XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
- XRPO: Pushing the Limits of GRPO with Targeted Exploration and Exploitation
- XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding
- XTransfer: Modality-Agnostic Few-Shot Model Transfer for Human Sensing at the Edge
- XYZFlow: Scaling Multidimensional Shortcut Flows for Efficient Generative Modeling
- You Can Learn Tokenization End-to-End with Reinforcement Learning
- You Don’t Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models
- You Don't Protect if You Don't Expect: Breaking the Key Assumption behind CLIP's Test-Time Defenses
- You Need Better Attention Priors
- Your Latent Reasoning is Secretly Policy Improvement Operator
- Z-Erase: Enabling Concept Erasure in Single Stream Diffusion Transformers
- ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
- ZeroDiff: Zero-Shot Time Series Reconstruction via Informed-Prior Diffusion
- Zero-Flow Encoders
- Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation
- Zero-shot Active Mapping via Fused 360-BEV Representations and Vision–Language Models
- Zero-Shot Off-Policy Learning
- Zero-Shot Rankability: Revealing Latent Ordinal Structure in Multimodal Large Language Models via Language
- Zero-Shot Text-to-Motion Evaluation using Video Language Models
- Zero-source LLM Hallucination Detection with Human-like Criteria Probing
- Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression
- Zeroth-Order Forward-Only SNN Training Inspiring Neuromorphic On-Chip Learning
- Zeroth-Order Optimization at the Edge of Stability
- ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models
- Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
- ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
- Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
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