大模型驱动的科研智能体，让科学发现更高效可靠。
Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents
- 构建融合领域知识与工具链的专用科研智能体
- 实现从假设生成到实验设计的全流程自动化
- 适合科研人员与跨学科团队提升研究效率
随着科学研究日益复杂，亟需创新工具来管理海量数据、促进跨学科协作并加速发现。大型语言模型正演变为基于大模型的科研智能体，可自动完成从假说生成、实验设计到数据分析与仿真的关键任务。与通用大模型不同，这些专用智能体融合领域知识、先进工具集和强验证机制，能处理复杂数据类型，保障可复现性，推动科学突破。本文系统综述了基于大模型的科研智能体在架构、设计、评估基准、应用及伦理方面的进展，强调其与通用智能体的差异及其在多学科研究中的推进作用。通过分析其发展历程与挑战，为研究者和实践者提供了一条高效、可靠且符合伦理的科学发现路径。
原文摘要 · Abstract (English)
As scientific research becomes increasingly complex, innovative tools are needed to manage vast data, facilitate interdisciplinary collaboration, and accelerate discovery. Large language models (LLMs) are now evolving into LLM-based scientific agents that automate critical tasks ranging from hypothesis generation and experiment design to data analysis and simulation. Unlike general-purpose LLMs, these specialized agents integrate domain-specific knowledge, advanced tool sets, and robust validation mechanisms, enabling them to handle complex data types, ensure reproducibility, and drive scientific breakthroughs. This survey provides a focused review of the architectures, design, benchmarks, applications, and ethical considerations surrounding LLM-based scientific agents. We highlight why they differ from general agents and the ways in which they advance research across various scientific fields. By examining their development and challenges, this survey offers a comprehensive roadmap for researchers and practitioners to harness these agents for more efficient, reliable, and ethically sound scientific discovery.
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