Kavli Affiliate: Blake Sherwin
| First 5 Authors: Licong Xu, Licong Xu, , ,
| Summary:
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.
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