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SciMARS:闭环科学发现多智能体递归自我优化

原文标题 · SciMARS: Multi-Agent Recursive Self-Improvement for Closed-Loop Scientific Discovery

作者: 吴伟民、刘汉 | 我们提出SciMARS,一种用于闭环科学发现中多智能体装置递归任务本地适配的框架。多智能体系统可协调分析、实验设计与验证,以应对科学证据的演变。然而,两个问题限制了其有效性:(i) 在没有共享内存的情况下,智能体会丢失观测数据并重复工作;(ii) 固定的角色流程、交接机制以及

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Artificial Intelligence

Title: SciMARS: Multi-Agent Recursive Self-Improvement for Closed-Loop Scientific Discovery

Abstract: We propose SciMARS, a framework for recursive task-local adaptation of multi-agent harnesses in closed-loop scientific discovery. Multi-agent systems coordinate analysis, experimental design, and verification as scientific evidence evolves. However, two problems limit their effectiveness: (i) without shared memory, agents lose observations and repeat work; (ii) fixed role procedures, handoffs, and memory policies perpetuate mistakes despite retained evidence. We address these problems with a shared evidence graph and separate component editors. SciMARS adapts roles and coordination; SciMARS + Memory adds a third editor for how agents record, retrieve, and receive evidence. Candidate probes guide composition and selection of the next harness. We evaluate scientific discovery through experimental-data replay and a digital twin. SciMARS + Memory resolves 12/18 GPT-6-Astra and 2/18 GLM-5.3 attempts on Autonomous Science Bench, compared with 8/18 and 1/18 for the base multi-agent team. On ScienceAgentBench and AppWorld, it solves 14/38 and 11/20 tasks, compared with 11/38 and 5/20 for the base team. These results support combining shared memory with harness adaptation across scientific and interactive tasks.

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