EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation
[Sztuczna inteligencja] Autorzy: Qing Zhao, Haowei Li, Weijian Deng i 2 więcej
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 inductively distilled into correction rules that revise the causal structures and mechanisms of each …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja