{
  "id": 13698,
  "url": "https://arxiv.org/abs/2607.22375v1",
  "title": "IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation",
  "summary": "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 an",
  "authors": "Varun Gumma, Navonil Majumder, Soumitra Sinhahajari, Soujanya Poria",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T15:03:14.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13698",
  "original_url": "https://arxiv.org/abs/2607.22375v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}