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IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation
arXiv cs.AI · Published · 3 min read
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- What happened
- IDEAgent is a multi-agent framework that generates research ideas by balancing quality and diversity simultaneously, outperforming baselines 3.89x on a new evaluation metric.
- Why it matters
- Researchers and engineers building AI systems for scientific discovery or automated ideation need better methods to avoid trivial or redundant concept generation.
- Watch out
- The paper is under review and not yet peer-published. Real-world applicability across domains beyond Computer Science remains undemonstrated.
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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
View a PDF of the paper titled IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation, by Varun Gumma and 3 other authors
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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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