EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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The paper introduces an experimental model‑class revision framework that jointly proposes structural edits to a hypothesis space and diagnostic experiments to test those edits. By coupling a class‑level distinguishability objective with anytime‑valid sequential evidence, the method only revises the model class after the current one is rejected. On 400 controlled dynamical environments, the approach achieves 89.5% exact recovery with 32 experiments, outperforming baselines and transferring well to unseen mechanisms, library insufficiency detection, and other benchmark tasks.
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