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Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents

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Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents
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Equipped with broad knowledge, flexible reasoning, and tool use, they have the potential to autonomously explore and solve scientific problems by repeatedly proposing hypotheses, testing them, and revising their beliefs in the light of the evidence. In current agents, however, these hypotheses, tests, and belief updates are buried in unstructured logs, and no mechanism lets the agent or the human researcher audit that process. Here we propose the Hypothesis Evolution Protocol (HEP), an agent harness that provides hypothesis generation, evaluation, and evolution as explicit, auditable operations.

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arXiv cs.AI
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Computer Science > Artificial Intelligence arXiv:2607.09195 (cs) [Submitted on 10 Jul 2026] Title:Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents Authors:Izumi Takahara, Teruyasu Mizoguchi View a PDF of the paper titled Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents, by Izumi Takahara and 1 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery. Equipped with broad knowledge, flexible reasoning, and tool use, they have the potential to autonomously explore and solve scientific problems by repeatedly proposing hypotheses, testing them, and revising their beliefs in the light of the evidence.

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