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Learning Scientific Exploration from Human Research Decision Trajectories — arXiv:2610.07184

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07184
License (as archived): CC0-1.0
Archived: 2026-10-07T11:23:36.917Z
SHA-256 of the archived text: 922115951c89eb97e6e1096c5f251fdbe8af7860af3678198cbed54926616aa0
Stable URL of this page: https://metraipolis.com/documento/dc_dc7affdd041e

How to cite

Learning Scientific Exploration from Human Research Decision Trajectories — arXiv:2610.07184 — arXiv (en), https://arxiv.org/abs/2610.07184 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dc7affdd041e (Metraipolis archive dc_dc7affdd041e, sha256 922115951c89eb97e6e1096c5f251fdbe8af7860af3678198cbed54926616aa0)

BibTeX

@misc{dcdc7affdd041e,
  title = {Learning Scientific Exploration from Human Research Decision Trajectories — arXiv:2610.07184},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07184},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dc7affdd041e; sha256 922115951c89eb97e6e1096c5f251fdbe8af7860af3678198cbed54926616aa0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07184. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A key challenge in building AI systems for scientific research is enabling $\textit{scientific exploration}$: the systematic process of investigating unknown phenomena or ideas to gain new knowledge through sequences of research decisions and actions. Yet this process is largely missing from existing scientific corpora; for example, research papers primarily record final outcomes rather than the trajectories that produced them. In this work, we introduce $\textbf{ResearchTrails}$, a dataset of $\textbf{human research trajectories constructed from Git repositories}$, where $\textbf{commit histories}$ serve as proxies for research exploration. We develop an automated and scalable pipeline that extracts structured research trajectories from repository commits, capturing successive changes to methods, experiments, and ablations. We characterize the resulting dataset and show that these trajectories contain meaningful signals about intermediate research decisions beyond what final papers reveal. We further demonstrate utilities of ResearchTrails in multiple use cases, including retrieving human research experience as external skills at test time and training models on research trajectories to improve generalization to new research decisions. Our results suggest a path toward AI systems that learn not only from the products of science, but from the evolving process of discovery itself.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.07184 — 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 2026-10-07T11:15:39.476Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).