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SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning


Official implementation of SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning


Abstract: Recent advances in large language models have enabled AI systems to achieve expert-level performance on domain-specific scientific tasks, yet these systems remain narrow and handcrafted. We introduce SciAgent, a unified multi-agent system designed for generalistic scientific reasoning—the ability to adapt reasoning strategies across disciplines and difficulty levels. SciAgent organizes problem solving as a hierarchical process: a Coordinator Agent interprets each problem’s domain and complexity, dynamically orchestrating specialized Worker Systems, each composed of interacting reasoning Sub-agents for symbolic deduction, conceptual modeling, numerical computation, and verification. These agents collaboratively assemble and refine reasoning pipelines tailored to each task. This work establishes SciAgent as a concrete step toward generalistic scientific intelligence—AI systems capable of coherent, cross-disciplinary reasoning at expert levels.

Overview

  • Conceptual contribution: We introduce generalistic scientific reasoning as a new paradigm for AI in science, emphasizing adaptability across domains and modalities.

  • Architectural innovation: We propose a Coordinator–Worker–Sub-agents hierarchy in which the Coordinator performs domain-adaptive routing and the Worker Systems self-assemble internal multi-agent pipelines.

  • Dynamic reasoning mechanism: We demonstrate self-assembling, feedback-driven reasoning loops that integrate symbolic deduction, conceptual modeling, and quantitative computation.

./assets/overview.png
SciAgent consists of a hierarchical multi-agent framework with a Coordinator Agent that routes problems to domain-specific Worker Systems. Each Worker System—Math, Physics, Chemistry, and General Exam—contains multiple Sub-agents (e.g., Generator, Reviewer, Image Analyser) collaborating through adaptive reasoning loops. The right panel summarizes key design principles: hierarchical meta-reasoning, modularity, and adaptive assembly.

TODO 🚀

All code releases will be made by November 20.

  • [] Release the code of SciAgent framework.

  • [] Release the code of Math Olympiad Worker System.

  • [] Release the code of Physics Olympiad Worker System.

  • [] Release the code of Chemistry Olympiad Worker System.

  • [] Release the code of General Exam Worker System.

Acknowledgement

This code is heavily inspired by Somlagents, IMO25, Physics-Supernova and SciToolAgent. Thank you for your outstanding work!

License

This repository is licensed under the Apache License Version 2.0. You are free to use, modify, and distribute this code in compliance with the terms and conditions of the Apache License Version 2.0.

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