From Tools to Taste: AI for Research in the Age of Agents
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With the development of large language models and agent systems, AI is moving from an auxiliary tool toward an agentic system that can take part in the research process. Organized around three capabilities—scientific taste, tool execution, and evidence verification—this talk presents early explorations by the Fudan NLP Lab: training AI to judge and conceive promising research ideas from research-community feedback; using SciAgentGym to evaluate and improve models’ long-horizon use of scientific tools; and using OpenNovelty to provide traceable, verifiable novelty evidence for peer review. The talk argues for a cautious path: let AI take on more of the exploration and execution, while leaving value judgments and final responsibility to scientists.