[2605.26340] ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence

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arXiv:2605.26340 (cs)

[Submitted on 25 May 2026]

Title:ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence

Authors:Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen, Chun-Liang Li, Palash Goyal, Mihir Parmar, Yiwen Song, Yale Song, Rajarishi Sinha, Parthasarathy Ranganathan, Burak Gokturk, Jinsung Yoon, Tomas Pfister<br>View a PDF of the paper titled ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence, by Rui Meng and 12 other authors

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Abstract:Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evaluation: fabricated citations, unreproducible scores, and method descriptions that diverge from the implementation. We address this through three contributions. First, Chain-of-Evidence (CoE), a verifiability framework requiring every claim to be traceable to its evidence source. Second, ScientistOne, an end-to-end autonomous research system that maintains evidence chains by construction throughout literature review, solution discovery, and paper writing. Third, CoE Audit, a post-hoc audit whose four integrity checks -- score verification, specification violation, reference verification, and method-code alignment -- apply uniformly to all systems. Across 75 papers spanning five systems and five frontier research tasks, every baseline exhibits at least one systematic failure mode: hallucinated reference rates reach 21%, score verification passes in as few as 42% of papers, and method-code alignment ranges from 20% to 80%. ScientistOne achieves zero hallucinated references (0/337), perfect score verification (12/12), and the highest method-code alignment (14/15), while matching or exceeding human expert performance on all five tasks. ScientistOne further generalizes to six additional tasks spanning medical imaging, fine-grained recognition, 3D perception, and language modeling, achieving state-of-the-art on Parameter Golf and gold medals on MLE-Bench tasks where baselines fail entirely.

Comments:<br>Project website: this https URL

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)

Cite as:<br>arXiv:2605.26340 [cs.AI]

(or<br>arXiv:2605.26340v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.26340

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Submission history<br>From: Rui Meng [view email]<br>[v1]<br>Mon, 25 May 2026 21:30:27 UTC (5,073 KB)

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