Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Abstract
We present a multi-agent system for automation of scientific research tasks, cmbagent (https://github.com/CMBAgents/cmbagent). 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.
Cite
@article{arxiv.2507.07257,
title = {Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery},
author = {Licong Xu and Milind Sarkar and Anto I. Lonappan and Íñigo Zubeldia and Pablo Villanueva-Domingo and Santiago Casas and Christian Fidler and Chetana Amancharla and Ujjwal Tiwari and Adrian Bayer and Chadi Ait Ekioui and Miles Cranmer and Adrian Dimitrov and James Fergusson and Kahaan Gandhi and Sven Krippendorf and Andrew Laverick and Julien Lesgourgues and Antony Lewis and Thomas Meier and Blake Sherwin and Kristen Surrao and Francisco Villaescusa-Navarro and Chi Wang and Xueqing Xu and Boris Bolliet},
journal= {arXiv preprint arXiv:2507.07257},
year = {2025}
}
Comments
Accepted contribution to the ICML 2025 Workshop on Machine Learning for Astrophysics. Code: https://github.com/CMBAgents/cmbagent Videos: https://www.youtube.com/@cmbagent HuggingFace: https://huggingface.co/spaces/astropilot-ai/cmbagent Cloud: https://cmbagent.cloud