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. 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.
Cite
@article{arxiv.2511.08151,
title = {SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning},
author = {Xuchen Li and Ruitao Wu and Xuanbo Liu and Xukai Wang and Jinbo Hu and Zhixin Bai and Bohan Zeng and Hao Liang and Leheng Chen and Mingrui Chen and Haitian Zhong and Xuanlin Yang and Xu-Yao Zhang and Liu Liu and Jia Li and Kaiqi Huang and Jiahao Xu and Haitao Mi and Wentao Zhang and Bin Dong},
journal= {arXiv preprint arXiv:2511.08151},
year = {2025}
}
Comments
1. To ensure result rigor, the model outputs require further evaluation by human experts. 2. The results may affect our conclusions and methods, thus necessitating a more detailed review. 3. We anticipate subsequent revisions may be substantial, potentially involving major adjustments to the methodology. Given the uncertainty surrounding the revision process, we decide to request a withdrawal