ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence
The paper titled 'ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence' presents a new framework for enhancing the verifiability of autonomous research outputs. It introduces the Chain-of-Evidence (CoE) framework and the ScientistOne system, which ensures that all claims are traceable to their evidence sources. The study demonstrates that ScientistOne outperforms existing systems in terms of reference accuracy and method alignment while achieving human-level performance across various research tasks.
- ▪The Chain-of-Evidence framework requires every claim to be traceable to its evidence source.
- ▪ScientistOne achieves zero hallucinated references and perfect score verification across tested papers.
- ▪The system generalizes to six additional tasks, achieving state-of-the-art results in multiple areas.
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Computer Science > Artificial Intelligence 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 View a PDF of the paper titled ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence, by Rui Meng and 12 other authors View PDF HTML (experimental) 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…
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