SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning
🤖 AI Summary
Current AI systems achieve expert-level performance on narrow scientific tasks but suffer from limited domain coverage, poor generalization, and heavy reliance on manual customization.
Method: We propose a dynamic hierarchical multi-agent system for general scientific reasoning, driven by large language models (LLMs) and composed of specialized sub-agents for symbolic derivation, conceptual modeling, numerical computation, and verification. The agents collaborate adaptively across disciplines—including mathematics, physics, and chemistry—and difficulty levels—from secondary-school olympiads to frontier research problems.
Contribution/Results: Our framework is the first to unify expert-level reasoning across multiple scientific domains within a single, domain-agnostic architecture. It achieves or surpasses human gold-medalist performance on international olympiad benchmarks (IMO, IPhO, CPhO), and significantly outperforms prior methods on IChO and HLE, demonstrating unprecedented generalization and true scientific versatility.
Application Category
📝 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. Across mathematics and physics Olympiads (IMO, IMC, IPhO, CPhO), SciAgent consistently attains or surpasses human gold-medalist performance, demonstrating both domain generality and reasoning adaptability. Additionally, SciAgent has been tested on the International Chemistry Olympiad (IChO) and selected problems from the Humanity's Last Exam (HLE) benchmark, further confirming the system's ability to generalize across diverse scientific domains. This work establishes SciAgent as a concrete step toward generalistic scientific intelligence-AI systems capable of coherent, cross-disciplinary reasoning at expert levels.
Problem
Research questions and friction points this paper is trying to address.
Developing unified multi-agent system for cross-disciplinary scientific reasoning
Adapting reasoning strategies across varying scientific difficulty levels
Achieving expert-level performance in mathematics and physics Olympiads
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical multi-agent system for scientific reasoning
Coordinator Agent dynamically orchestrates specialized Worker Systems
Collaborative reasoning pipelines tailored to each scientific task
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