EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation· EvoSCM：通过因果模型进化和实验的科学信念修订
Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in free-form text, leaving their beliefs implicit and difficult to test or revise. We introduce EvoSCM, which equips scientific agents with explicit structural causal models that evolve as new experimental evidence is collected. EvoSCM maintains a population of competing SCM hypotheses, each encoding a candidate causal explanation of the environment, and evolves them through a closed discovery loop. In each round, the agent abduces latent mechanisms from accumulated evidence, designs discriminative interventions, and commits to falsifiable predictions that it tests through experimentation. Discrepancies between prediction and observation are in
AI科学代理通过因果模型的进化与实验学习科学信念修订。
- 核心方法
- 引入EvoSCM，利用显式的结构因果模型（SCM），通过假设竞争、从证据中推断隐藏机制、设计干预实验和测试可证伪预测来进化科学信念。
- 适合谁读
- 研究者
- 要解决的问题
- 现有的LLM科学代理难以显式测试或修订其科学信念。
- 关键实验
- 未提供
- 主要贡献
- 提出了一种新的科学代理框架EvoSCM，能够显式管理、测试和修订科学信念。
- 意义与局限
- EvoSCM推进了AI在科学探索中的应用，但需要进一步的实验验证。