IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation

arXiv:2607.22375v2 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced the automation of scientific discovery. However, existing systems share a core limitation: they optimize ideas independently for either Quality or Diversity. This often results in closely related ideas or large sets of trivial, unsound, or unclear concepts. We instead argue that research ideation should be framed as a conjunction of Quality and Diversity objectives, i.e., a Quality-Diversity (QD) search. Accordingly, we introduce IDEAgent, a multi-agent framework that tracks idea evolut

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