AI as a Tool for Mathematics, Computer Science, and Machine Learning

Modern AI systems increasingly assist researchers with coding, exposition, and fragments of mathematical reasoning, yet turning these capabilities into dependable research progress remains nontrivial. This workshop focuses on AI as a practical research instrument for the mathematics/CS/ML community: not merely improving theorem-proving benchmarks, but developing transferable, reproducible workflows that help researchers generate, stress-test, and refine real results. The program will cover (i) AI-assisted mathematical research workflows, including iterative verification loops, decomposition and self-critique, multi-agent strategies, and common failure modes with concrete detection/mitigation tactics; (ii) tool-augmented reasoning, integrating LLMs with computation (code, symbolic algebra, numerics), literature navigation, and proof assistants (e.g., Lean) to reduce hallucinations and improve reliability; and (iii) research acceleration across ML/CS, including derivations, counterexample search, and experiment design methods that generalize across subfields. The workshop is structured as a full-day hybrid event with confirmed in-person invited talks, a demo/poster session featuring accepted contributions (4-page submissions emphasizing usable workflows), and a structured debate and panel on whether AI-generated analyses and conclusions will become as trustworthy as those of leading theoretical researchers within five years. The intended outcome is a durable community resource: a shared set of actionable practices for rigorous AI-assisted research.