📄ArXiv AI•May 27, 2026Stalecollected in 23h

ScientistOne: Autonomous Research via Chain-of-Evidence Framework

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#autonomous-agents #ai-researchscientistonescientistone mle-bench

💡Learn how to eliminate AI hallucinations in research tasks using the new Chain-of-Evidence verification framework.

⚡ 30-Second TL;DR

What Changed

Introduces Chain-of-Evidence (CoE) to ensure every claim is traceable to a source.

Why It Matters

This research addresses the critical 'verifiability gap' in autonomous AI agents, providing a robust framework for building reliable research tools that minimize hallucinations in scientific writing.

What To Do Next

Incorporate a Chain-of-Evidence verification step in your RAG pipelines to audit source traceability before finalizing AI-generated reports.

Who should care:Researchers & Academics

Key Points

•Introduces Chain-of-Evidence (CoE) to ensure every claim is traceable to a source.
•Implements a post-hoc CoE Audit to verify scores, references, and method-code alignment.
•Achieved zero hallucinated references and state-of-the-art performance on MLE-Bench tasks.
•Outperforms existing baselines in method-code alignment, reaching up to 93% accuracy.

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