Open Source Planning & Control System With Language Agents for Autonomous Scientific Discovery

MCML Authors

Thomas Meier

Dr.

Science Manager Collaborations, PhD Coordinator, and DEI Official

Abstract

We present a multi-agent system for automation of scientific research tasks, cmbagent (this https URL). The system is formed by about 30 Large Language Model (LLM) agents and implements a Planning & Control strategy to orchestrate the agentic workflow, with no human-in-the-loop at any point. Each agent specializes in a different task (performing retrieval on scientific papers and codebases, writing code, interpreting results, critiquing the output of other agents) and the system is able to execute code locally. We successfully apply cmbagent to carry out a PhD level cosmology task (the measurement of cosmological parameters using supernova data) and evaluate its performance on two benchmark sets, finding superior performance over state-of-the-art LLMs. The source code is available on GitHub, demonstration videos are also available, and the system is deployed on HuggingFace and will be available on the cloud.

inproceedings XSL+25

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ML4Astro @ICML 2025

Machine Learning for Astrophysics at the 42nd International Conference on Machine Learning. Vancouver, Canada, Jul 13-19, 2025.

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Authors

L. Xu • M. Sarkar • A. I. Lonappan • Í. Zubeldia • P. Villanueva-Domingo • S. Casas • C. Fidler • C. Amancharla • U. Tiwari • A. Bayer • C. A. Ekioui • M. Cranmer • A. Dimitrov • J. Fergusson • K. Gandhi • S. Krippendorf • A. Laverick • J. Lesgourgues • A. Lewis • T. Meier • B. Sherwin • K. Surrao • F. Villaescusa-Navarro • C. Wang • X. Xu • B. Bolliet

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Links

PDF

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In Collaboration

 Google
 Infosys

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Research Area

 A3 | Computational Models