构建可跨学科推理的智能科研系统，达到奥数金牌水平。
SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning
- 用协调员+专业子代理分层协作，动态组合推理流程。
- 在数学物理奥赛中表现超越人类金牌选手。
- 适合研究通用科学智能与多任务推理系统的学者。
大语言模型虽在特定科学任务上达到专家水平，但普遍局限且依赖人工设计。本文提出SciAgent，一种面向通用科学推理的统一多智能体系统，具备跨学科和多难度适应能力。系统将问题求解组织为层级过程：协调员智能体解析问题领域与复杂度，动态调度由符号推演、概念建模、数值计算和验证等子代理组成的专用工作系统，协同构建并优化适配各任务的推理链。在国际数学奥林匹克（IMO）、国际数学竞赛（IMC）、国际物理奥林匹克（IPhO）、中国物理奥林匹克（CPhO）中，SciAgent持续达到或超过人类金牌选手表现，验证了其领域泛化与推理策略自适应能力。此外，在国际化学奥林匹克（IChO）及人类最后考试（HLE）部分题目上测试，进一步证明其在多样化科学领域的泛化潜力。本工作推动通用科学智能发展，使AI能在专家水平实现连贯跨学科推理。
原文摘要 · Abstract (English)
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.
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