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IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation — arXiv:2610.04074

Source: arXiv
Original: https://arxiv.org/abs/2610.04074
License (as archived): CC0-1.0
Archived: 2026-10-06T05:28:00.687Z
SHA-256 of the archived text: 536b7c359c3b17c733c071c4a50ea92823137db06bc9b1adf4b95fa8c6963b1c
Stable URL of this page: https://metraipolis.com/documento/dc_f0fa446dedbf

How to cite

IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation — arXiv:2610.04074 — arXiv, https://arxiv.org/abs/2610.04074 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f0fa446dedbf (Metraipolis archive dc_f0fa446dedbf, sha256 536b7c359c3b17c733c071c4a50ea92823137db06bc9b1adf4b95fa8c6963b1c)

BibTeX

@misc{dcf0fa446dedbf,
  title = {IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation — arXiv:2610.04074},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.04074},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f0fa446dedbf; sha256 536b7c359c3b17c733c071c4a50ea92823137db06bc9b1adf4b95fa8c6963b1c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.04074. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge. We isolate research ideation as a standalone task and build our solution on the intuition that a challenge in one field can often be addressed by a mechanism that solved an analogous challenge in another. Accordingly, we introduce IdeaScientist, which decomposes ideation into gap finding, innovation, and report writing, and trains each role with reinforcement learning. These roles identify limitations in related work, draw solution intuitions from analogous problem settings, and develop those intuitions into complete research proposals. To facilitate discovery of insights across domains, we construct the Svalbard Idea Vault, a corpus of 2.77M decomposed research ideas for retrieval, training, and temporally controlled evaluation. Our evaluation restricts access to literature available before a cutoff date and assesses how closely proposed directions align with those later explored in 15K papers authored by human researchers. On Qwen3.6-27B, IdeaScientist outperforms the strongest open-source autoresearch baseline by 14.0%, driven mainly by gains in novelty. On this 27B open backbone, IdeaScientist even outperforms Claude Code SDK with Claude-4.8-Opus and Codex SDK with GPT-5.4, by up to 5.9%.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.04074 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:20.247Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).