SCALE: SCALABLE LEARNING AND OPTIMIZATION FOR EFFICIENT MULTIMODAL AI AGENTS

This workshop seeks to bring together researchers from diverse background to explore (but not limited to) emerging topics in, a) multi-modal agentic learning: learning algorithms, pipelines, and architectures for multimodal agents, spanning pretraining and fine-tuning to test-time tuning and adaptation; b) Efficient agentic AI systems: developing scalable and verifiable agentic AI systems across heterogeneous compute platforms with limited compute and memory budget, ; c) scaling of multi-modal agents: understanding and improving the test-time scaling and reasoning capabilities of multi-modal agentic systems, mixture-of-agents for task scaling; d) multi-modal agents for planning: pushing the boundaries of real life physical reasoning and planning for agentic AI. e) evaluation and benchmarking: principled metrics and benchmarks for reasoning, memory, robustness, and efficiency in multimodal agents; f) memory of agents: understanding and improving multi-modal agentic memory for reasoning capabilities.