Bridging Literature and the Universe Via A Multi-Agent Large Language Model System

TL;DR

This paper introduces SimAgents, a multi-agent LLM system that automates the extraction of cosmological simulation parameters from literature, improving efficiency and reducing errors in physics research.

Contribution

The paper presents a novel multi-agent system that automates parameter extraction and validation from complex literature, enhancing cosmological simulation workflows.

Findings

SimAgents achieves high accuracy in parameter extraction from diverse papers.

The system accelerates the setup process for cosmological simulations.

Demonstrated effectiveness on a new dataset of 40+ published simulation papers.

Abstract

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.