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

2026-07-10

AI Summary

Current large language model (LLM) agents lack an auditable hypothesis-driven reasoning mechanism in scientific discovery, rendering their inference processes opaque and difficult to verify. This work proposes the Hypothesis Evolution Protocol (HEP), which, for the first time, formalizes hypothesis generation, experimental testing, evidence integration, and belief updating—core components of scientific reasoning—as explicit, structured, and auditable operations. Embedded within an LLM agent architecture, HEP enables end-to-end transparent reasoning and supports cross-task generalization. Evaluated on materials science tasks, HEP agents fully implement a closed-loop hypothesis–test–belief cycle and significantly outperform conventional planning-based agents, achieving notable advances in both auditability and generalization capability.

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. 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. On materials-science research tasks, a HEP-equipped agent operates the hypothesis--test--evidence--belief cycle that planning-style agents lack, generalizes across research questions, and exploits the protocol more fully as the base LLM becomes more capable. These results mark a step toward auditable AI scientists, whose scientific reasoning can be inspected, verified, and built upon.

Problem

• auditable AI
• scientific discovery
• hypothesis evolution
• LLM agents
• reasoning traceability

Innovation

• Hypothesis Evolution Protocol
• Auditable AI
• LLM Agents
• Scientific Reasoning
• Autonomous Discovery