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Bioinfoysis Technical Report — arXiv:2609.03871

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.03871
License (as archived): CC0-1.0
Archived: 2026-10-17T01:03:03.641Z
SHA-256 of the archived text: ebbb6f3af0b2788adf52cfc0e2da569fff227d9ce892266bc9ef9d26c2ae7ae8
Stable URL of this page: https://metraipolis.com/documento/dc_7fdbdde30fa6

How to cite

Bioinfoysis Technical Report — arXiv:2609.03871 — arXiv (en), https://arxiv.org/abs/2609.03871 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7fdbdde30fa6 (Metraipolis archive dc_7fdbdde30fa6, sha256 ebbb6f3af0b2788adf52cfc0e2da569fff227d9ce892266bc9ef9d26c2ae7ae8)

BibTeX

@misc{dc7fdbdde30fa6,
  title = {Bioinfoysis Technical Report — arXiv:2609.03871},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.03871},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7fdbdde30fa6; sha256 ebbb6f3af0b2788adf52cfc0e2da569fff227d9ce892266bc9ef9d26c2ae7ae8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.03871. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language model agents have shown promise in bioinformatics, but most existing systems focus primarily on producing final answers, treating planning, tool use, and code execution as transient interactions. This design is poorly suited to long-horizon bioinformatics tasks, where conclusions must remain connected to the data, computations, and intermediate evidence that support them. We introduce \textbf{Bioinfoysis}, a multi-agent harness that represents each request as a persistent, artifact-grounded analysis run. Bioinfoysis combines global planning with step-wise, evidence-driven replanning: the planner maintains an executable checklist and revises pending steps using structured handoffs returned after each worker execution. These handoffs bind intermediate results to their responsible agent, checklist step, and plan generation, preventing stale evidence from being silently reused after replanning. A controlled runtime validates generated scripts, tables, and figures before they are used in downstream analysis or reporting, while role-specific context, persistent memory, and governed bioinformatics skills support reliable execution over long analysis trajectories. We evaluate Bioinfoysis on BixBench and two question-answering tracks of LAB-Bench 2. On BixBench, Bioinfoysis achieves state-of-the-art accuracy of 82.4\%. Across four underlying language models, Bioinfoysis increases average accuracy from 27.81\% to 64.13\% on SeqQA2 and from 3.13\% to 31.25\% on DbQA2. These results demonstrate that reliable bioinformatics automation depends not only on model capability, but also on the harness that governs planning, execution, memory, and evidence flow. We hope that the emergence of Bioinfoysis will play a driving and leading role in the development of the bioinformatics community. Our demo website can be seen in this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-04 in cs.MA).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2609.03871 — 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 2026-10-17T00:22:10.236Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).