用多智能体系统自动提取论文参数并生成可运行脚本
Bridging Literature and the Universe Via A Multi-Agent Large Language Model System
- 设计多智能体协作系统，解析论文中的宇宙学模拟参数
- 在40+篇论文数据集上实现高准确率参数提取与脚本生成
- 适合天体物理研究者快速复现复杂模拟，节省大量人工时间
随着宇宙学模拟及其软件日趋复杂，物理学家需在海量文献和用户手册中手动查找模拟参数，再转换为可执行脚本，过程耗时且易错。为此，我们提出SimAgents——一个用于自动化参数配置与初步分析的多智能体系统。该系统由具备物理推理、软件验证和工具执行能力的专用大模型智能体构成，通过结构化通信确保提取参数在物理意义、内部一致性和软件兼容性方面均达标。我们构建了一个包含40余项已发表论文中模拟案例的参数提取评估数据集，涵盖多种模拟类型。实验表明，SimAgents在该数据集上表现优异，展现出显著提升科研效率的潜力。完整系统与数据集已在GitHub公开：https://github.com/xwzhang98/SimAgents。演示视频见：https://youtu.be/w1zLpm_CaWA。
原文摘要 · Abstract (English)
As cosmological simulations and their associated software become increasingly complex, physicists face the challenge of searching through vast amounts of literature and user manuals to extract simulation parameters from dense academic papers, each using different models and formats. Translating these parameters into executable scripts remains a time-consuming and error-prone process. To improve efficiency in physics research and accelerate the cosmological simulation process, we introduce SimAgents, a multi-agent system designed to automate both parameter configuration from the literature and preliminary analysis for cosmology research. SimAgents is powered by specialized LLM agents capable of physics reasoning, simulation software validation, and tool execution. These agents collaborate through structured communication, ensuring that extracted parameters are physically meaningful, internally consistent, and software-compliant. We also construct a cosmological parameter extraction evaluation dataset by collecting over 40 simulations in published papers from Arxiv and leading journals that cover diverse simulation types. Experiments on the dataset demonstrate a strong performance of SimAgents, highlighting its effectiveness and potential to accelerate scientific research for physicists. Our demonstration video is available at: https://youtu.be/w1zLpm_CaWA. The complete system and dataset are publicly available at https://github.com/xwzhang98/SimAgents.
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