基于证据传递的协作式原理演化用于科学发现

中文摘要 AI 辅助

基于大型语言模型（LLM）的智能体有望实现科学发现的自动化，然而探索庞大的假设空间仍然代价高昂。现有的原理演化方法加速了这一循环，但它们是顺序操作的，这限制了探索的广度，并在难题上浪费了墙钟时间。为解决这一问题，我们将协作式科学发现表述为并行原理演化分支之间的证据传递。我们提出了COEVOLVE，它通过一个协调核心在并行分支之间实现这种传递。通过整合基于信息价值门控的路由和基于上下文折扣的似然注入，COEVOLVE使分支能够通过共享测量进行协作，同时保持各自的原理后验独立。在匹配的评估预算下，跨六个科学发现任务，COEVOLVE的平均解质量为66.5%，而单分支原理演化为57.0%，在GPT-5.6-Terra骨干上实现了1.80倍的平均墙钟加速；在委托给自主研究框架的五个自动研究任务中，它是唯一一个在每个任务上平均得分均高于已发表SOTA基准的臂。这些结果确立了证据共享何时加速并行发现，以及何时需要传递保障措施来限制负面或惰性传递。

英文摘要

Large Language Model (LLM)-based agents promise to automate scientific discovery, yet exploring the vast hypothesis space remains costly. Existing principle-evolution methods accelerate this loop, but operate sequentially, which caps exploration breadth and wastes wall-clock time on challenging problems. To address this, we formulate collaborative scientific discovery as evidence transfer between parallel principle-evolution branches. We present COEVOLVE, which realizes this transfer through a coordination core over parallel branches. By integrating value-of-information-gated routing and context-discounted likelihood injection, COEVOLVE enables branches to collaborate through shared measurements while keeping their principle posteriors separate. Across six scientific-discovery tasks under a matched evaluation budget, COEVOLVE attains a mean solution quality of 66.5% versus 57.0% for single-branch principle evolution, with a 1.80x mean wall-clock speedup on the GPT-5.6-Terra backbone; on five auto-research tasks delegated to an autonomous research harness, it is the only arm whose mean stays above the published SOTA anchor on every task. These results establish when evidence sharing accelerates parallel discovery and when transfer safeguards are necessary to limit negative or inert transfers

发表机构

• Zhejiang University（浙江大学）
• Westlake University（西湖大学）

机构由 AI 辅助整理，请以论文原文为准。