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This arXiv paper proposes InsightAgents, a multi-agent LLM system that gives each agent expertise distilled from graphs of related papers, then has them debate and refine research proposals. Its evaluation is limited: pairwise comparisons on 100 NLP topics, judged by two LLMs rather than domain experts, so the reported gains show relative preference under this setup, not proven research quality. Readers interested in automated scientific discovery should look at whether the literature-grounding approach holds up under human expert review.
Distill, Debate, Discover: Grounding Multi-Agent Research Ideation in Graph-Distilled Insights
arXiv:2610.33366v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support research idea generation and scientific discovery. Multi-agent debate offers a collaborative approach to this task by bringing together diverse perspectives to develop research ideas. However, a key challenge is how to equip agents with distinct and meaningful expertise to guide their contributions. Inspired by how researchers draw on accumulated research experience during collaboration, we introduce InsightAgents, a framework that constructs literature-grounded agent expertise by distilling insights from graphs of related papers. Guided by insights that trace evolving research approaches, reveal underlying assumptions, and identify unresolved challenges, within a line of research, agents jointly identify a research problem and propose, challenge, and refine potential approaches through debate to develop a research proposal. We evaluate InsightAgents against three research idea generation frameworks on 100 NLP research topics through pairwise comparisons by two LLM judges. In both online and offline knowledge settings, InsightAgents achieves the highest average score across the five evaluation dimensions under both judges.
Abstract:Large language models (LLMs) are increasingly used to support research idea generation and scientific discovery. Multi-agent debate offers a collaborative approach to this task by bringing together diverse perspectives to develop research ideas. However, a key challenge is how to equip agents with distinct and meaningful expertise to guide their contributions. Inspired by how researchers draw on accumulated research experience during collaboration, we introduce InsightAgents, a framework that constructs literature-grounded agent expertise by distilling insights from graphs of related papers. Guided by insights that trace evolving research approaches, reveal underlying assumptions, and identify unresolved challenges, within a line of research, agents jointly identify a research problem and propose, challenge, and refine potential approaches through debate to develop a research proposal. We evaluate InsightAgents against three research idea generation frameworks on 100 NLP research topics through pairwise comparisons by two LLM judges. In both online and offline knowledge settings, InsightAgents achieves the highest average score across the five evaluation dimensions under both judges.
Computation and Language (cs.CL)
arXiv:2610.33366 [cs.CL]
(or arXiv:2610.33366v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.33366 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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