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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26340 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | l__DmxPuh68Z |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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