Metraipolis › archive › dc_1981bd77155f

YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents — arXiv:2610.01097

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.01097
License (as archived): CC0-1.0
Archived: 2026-10-06T12:03:44.320Z
SHA-256 of the archived text: ff041618f4e0c1739cb2ca6f106773a651b033a2af7626f8634e6a710a269843
Stable URL of this page: https://metraipolis.com/documento/dc_1981bd77155f

How to cite

YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents — arXiv:2610.01097 — arXiv (en), https://arxiv.org/abs/2610.01097 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1981bd77155f (Metraipolis archive dc_1981bd77155f, sha256 ff041618f4e0c1739cb2ca6f106773a651b033a2af7626f8634e6a710a269843)

BibTeX

@misc{dc1981bd77155f,
  title = {YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents — arXiv:2610.01097},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.01097},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1981bd77155f; sha256 ff041618f4e0c1739cb2ca6f106773a651b033a2af7626f8634e6a710a269843},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.01097. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

End-to-end research agents can now produce complete scientific papers, yet manuscript claims often diverge from executed experiments. This gap is structural: research state, failure histories, and claim-evidence alignment are not maintained as persistent, verifiable state across long-horizon pipelines. We present YouRA (Your Research Agent), an architecture for stateful, evidence-traceable autonomous research. YouRA preserves research state, execution evidence, and failure history across the research trajectory by integrating three components: a Verification State Architecture (VSA) that tracks hypotheses, gates, and evidence pointers; an Independent Controller that turns state and reflection records into lifecycle, recovery, and debate/review control while separating control from execution; and Stateful Reflection that logs failures as structured lessons and routes recovery through bounded repair, redesign, or reset. On MLR-Bench's predefined ten-task end-to-end subset, YouRA improves over both MLR-Agent and AI Scientist V2 on scalar Overall across all three matched backbones. An automated diagnostic using MLR-Bench's hallucination taxonomy reports intersection/union counts for four fact-based failure types, and data-provenance diagnostic shows more real-data-based outputs. Ablating each of the four components (the VSA, the Independent Controller, MCP tool access, and reflection-guided recovery) supports their separable contributions. Removing either core-state component drops YouRA below the full system. Code: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-02 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.01097 — 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 2026-10-06T10:21:27.912Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).