arXiv cs.AI· Yuxiao Hu, Qian Li, Ding Wang, Siheng Chen, Linfeng Zhang, Jinyue Yan, Yuntian Chen·· 4 小时前AI 评分36
SCOPE 与 ClosureBench：从方程搜索走向猜想引导的科学发现
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
AI 导读
研究者提出 SCOPE 框架，让多个科学家智能体独立开展猜想驱动的方程建模、提问与主动实验，并通过交换证据修正猜想，最终整合为科学报告。配套的 ClosureBench 基准包含 60 个封闭发现任务、覆盖五个科学领域，在 OpenFOAM 中控制实验条件获取新数据，并提出证据忠实性、机理洞察和实验有效性三项指标。
正文 · 原文
Abstract:Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, revised, or rejected. A conjecture may admit multiple mathematical realizations, and similarly accurate equations may imply different explanations. This motivates a shift from refining individual expressions to developing and testing the conjectures that guide them. We introduce SCOPE, a framework that sustains multiple conjecture-driven research trajectories. Multiple scientist agents independently pursue conjectures through equation modeling, question formulation, and active experimentation, then exchange evidence to revise their conjectures and research directions. Their findings are consolidated into a scientific report linking models, conclusions, and evidence. Existing equation-discovery benchmarks primarily assess recovered expressions, leaving the evidence and scientific conclusions established through the investigation underexamined. We therefore introduce ClosureBench, an interactive benchmark of 60 closure-discovery tasks across five scientific domains. ClosureBench focuses on closure-discovery tasks to give equation discovery a concrete scientific purpose. Each task embeds an unknown algebraic relation in a known PDE system, where agents control experimental conditions in OpenFOAM to acquire new data for equation discovery. Beyond equation accuracy, ClosureBench proposes three metrics to evaluate the discovery process: evidence faithfulness, mechanism insight, and experimental effectiveness. Compared with diverse baselines, SCOPE achieves higher equation accuracy on both ClosureBench and a real-world scientific discovery case, while producing more accurate, reliable, and effective scientific discoveries.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.18938 [cs.AI] |
| (or arXiv:2610.18938v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18938 arXiv-issued DOI via DataCite |
Submission history
From: Yuxiao Hu [view email]
[v1]
Tue, 13 Oct 2026 12:46:49 UTC (753 KB)
[v2]
Mon, 2 Nov 2026 07:31:34 UTC (754 KB)
[v3]
Thu, 5 Nov 2026 05:42:31 UTC (754 KB)
来源：arXiv cs.AI · arxiv.org