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BioDyad: Synchronize Biomedical Discovery and Machine Learning Engineering — arXiv:2609.31939

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31939
License (as archived): CC0-1.0
Archived: 2026-10-07T20:55:00.052Z
SHA-256 of the archived text: 70c49298b039138279fc56e928840b64fa7c71eb63ae7db016a66a17a1a626cb
Stable URL of this page: https://metraipolis.com/documento/dc_2ed065238bd2

How to cite

BioDyad: Synchronize Biomedical Discovery and Machine Learning Engineering — arXiv:2609.31939 — arXiv (en), https://arxiv.org/abs/2609.31939 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ed065238bd2 (Metraipolis archive dc_2ed065238bd2, sha256 70c49298b039138279fc56e928840b64fa7c71eb63ae7db016a66a17a1a626cb)

BibTeX

@misc{dc2ed065238bd2,
  title = {BioDyad: Synchronize Biomedical Discovery and Machine Learning Engineering — arXiv:2609.31939},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31939},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ed065238bd2; sha256 70c49298b039138279fc56e928840b64fa7c71eb63ae7db016a66a17a1a626cb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31939. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Agentic biomedical machine learning (ML) draws on complementary advances in biomedical evidence acquisition and executable program search. Existing systems connect aspects of these capabilities, but coordinating them throughout program search remains challenging. New evidence must guide candidate construction, execution outcomes must inform subsequent discovery and reuse, and validation demands must fit the search budget. We introduce BioDyad, which couples biomedical discovery and ML engineering through two hierarchies within Monte Carlo graph search. Its scientific hierarchy combines prior biomedical guidance with iterative discovery, then links biomedical plans to execution outcomes in memory for reuse across candidates. Its engineering hierarchy moves candidate programs from smoke execution, through train/validation evaluation, to full-data retraining. We evaluate BioDyad on the 76-task BioXArena benchmark under a two-hour per-task budget with three matched LLM backends. It achieves the highest penalized all-task score and task success rate among four agent methods and a one-shot baseline under each backend. These results support coordinating biomedical discovery and ML engineering to integrate external knowledge into executable programs across heterogeneous biomedical tasks.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.31939 — 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 2026-10-07T20:29:00.681Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).