arXiv Machine Learning By Yuxiao Hu, Qian Li, Ding Wang, Siheng Chen, Linfeng Zhang, Jinyue Yan, Yuntian Chen

Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench

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The paper introduces SCOPE, a framework that enables multiple scientist agents to pursue conjecture-driven research trajectories by modeling equations, formulating questions, and conducting active experiments, then sharing evidence to refine their conjectures. It also presents ClosureBench, an interactive benchmark of 60 closure-discovery tasks across five scientific domains, designed to evaluate equation discovery beyond accuracy by measuring evidence faithfulness, mechanism insight, and experimental effectiveness. Experiments show that SCOPE outperforms diverse baselines on both ClosureBench and a real-world scientific discovery case, yielding more accurate, reliable, and effective scientific discoveries.

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