EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation
Scientific discovery depends on the ability to form hypotheses, test them through experiments, and revise them when evidence disagrees. Existing LLM agents support this process by improving their reasoning or actions, but their scientific beliefs are often scattered across free-form reasoning and difficult to update coherently. This makes it difficult to identify what failed, what should change, and whether revisions remain consistent with prior evidence. We introduce EvoSCM, which represents scientific beliefs as a population of structural causal model (SCM) hypotheses that can be tested and revised across experiments. EvoSCM formulates scientific discovery as a closed loop in which caus...
| Authors | Qing Zhao, Haowei Li, Weijian Deng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 September 2026 |
| Last updated at source | 28 September 2026 |
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