IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation

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Computer Science > Artificial Intelligence

arXiv:2607.22375v1 (cs)

[Submitted on 24 Jul 2026]

Title: IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation

Authors: Varun Gumma , Navonil Majumder , Soumitra Sinhahajari , Soujanya Poria

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Abstract: Large Language Models (LLMs) have significantly automated the process of scientific discovery over the past few years. However, existing systems share one core limitation: they generate and optimize ideas independently for either Quality or Diversity. This often leads to the generation of ideas in close proximity to one another or to a large set of trivial, unsound, or unclear concepts. In this work, we instead argue that research ideation should be treated as a conjunction of both objectives and framed as a Quality-Diversity (QD) search. In line with this perspective, we introduce IDEAgent, a multi-agent framework that manages the evolution of ideas through lineages. We jointly drive Quality using multi-objective feedback for dedicated repair and refinement, while Diversity is achieved through lightweight sequential memory and explicit comparison against completed ideas, their historical ancestors, and rejected proposals. To systematically ev

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