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

• URL: http://arxiv.org/abs/2507.08958v2
• Date: Tue, 15 Jul 2025 22:55:30 GMT
• Title: Bridging Literature and the Universe Via A Multi-Agent Large Language Model System
• Authors: Xiaowen Zhang, Zhenyu Bi, Patrick Lachance, Xuan Wang, Tiziana Di Matteo, Rupert A. C. Croft,
• Abstract summary: Physicists face the challenge of searching through vast amounts of literature to extract simulation parameters from dense academic papers.<n>We introduce SimAgents, a multi-agent system designed to automate both parameter configuration from the literature and preliminary analysis for cosmology research.<n>SimAgents is powered by specialized LLM agents capable of physics reasoning, simulation software validation, and tool execution.
• Score: 5.935305898559785
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Models [0.07315096254838022]
  SimuGen is a framework that automatically generates accurate Simulink simulation code by leveraging both the visual Simulink diagram and domain knowledge.<n>Our source code is publicly available at https://github.com/renxinxing123/SimuGen_beta.
  arXiv  Detail & Related papers  (2025-05-28T00:35:43Z)
• AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations [12.492232195149661]
  We present AgentDynEx, an AI system that helps set up simulations from user-specified mechanics and dynamics.<n>A technical evaluation found that nudging enables simulations to have more complex mechanics and maintain its notable dynamics compared to simulations without nudging.
  arXiv  Detail & Related papers  (2025-04-13T17:26:35Z)
• The AI Cosmologist I: An Agentic System for Automated Data Analysis [0.0]
  The AI Cosmologist implements a complete pipeline from idea generation to experimental evaluation and research dissemination.<n>Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies.<n>Results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery.
  arXiv  Detail & Related papers  (2025-04-04T13:12:08Z)
• MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science [62.96434290874878]
  Current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks.<n>We develop a new framework, named Multi-Modal Scientific Reasoning with Physics Perception and Simulation (MAPS) based on an MLLM.<n>MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator.
  arXiv  Detail & Related papers  (2025-01-18T13:54:00Z)
• ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono [8.922927652378544]
  ChronoLlama introduces a novel framework that customizes the open-source LLMs, specifically for code generation, paired with PyChrono for multi-physics simulations.<n>This integration aims to automate and improve the creation of simulation scripts, thus enhancing model accuracy and efficiency.<n> Empirical results indicate substantial enhancements in simulation setup speed, accuracy of the generated codes, and overall computational efficiency.
  arXiv  Detail & Related papers  (2025-01-07T10:39:14Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Embed and Emulate: Contrastive representations for simulation-based inference [11.543221890134399]
  This paper introduces Embed and Emulate (E&E), a new simulation-based inference ( SBI) method based on contrastive learning. E&E learns a low-dimensional latent embedding of the data and a corresponding fast emulator in the latent space. We demonstrate superior performance over existing methods in a realistic, non-identifiable parameter estimation task.
  arXiv  Detail & Related papers  (2024-09-27T02:37:01Z)
• DrEureka: Language Model Guided Sim-To-Real Transfer [64.14314476811806]
  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball.
  arXiv  Detail & Related papers  (2024-06-04T04:53:05Z)
• LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins [4.773175285216063]
  This paper presents a novel framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. The proposed approach enhances the usability of simulation model by infusing it with knowledges from LLM. The system has the potential to increase user-friendliness and reduce the cognitive load on human users.
  arXiv  Detail & Related papers  (2024-05-28T11:59:40Z)
• Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs [75.7104463046767]
  This paper proposes a novel learning based simulation model that characterizes the varying spatial and temporal dependencies in particle systems. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb.
  arXiv  Detail & Related papers  (2023-05-21T03:51:03Z)
• Simulation Intelligence: Towards a New Generation of Scientific Methods [81.75565391122751]
  "Nine Motifs of Simulation Intelligence" is a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery.
  arXiv  Detail & Related papers  (2021-12-06T18:45:31Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.