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.

翻译：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科学发现。大语言模型（LLMs）正逐步演化为基于大语言模型的科学智能体，能够自动化执行从假设生成、实验设计到数据分析与模拟等一系列关键任务。与通用大语言模型不同，这类专用智能体整合了领域特定知识、先进工具集及鲁棒的验证机制，使其能够处理复杂数据类型、确保结果可复现性并推动科学突破。本文聚焦综述基于大语言模型的科学智能体的体系架构、设计范式、评估基准、应用场景及伦理考量，重点阐明其与通用智能体的本质差异，以及它们如何推动多学科领域的研究进展。通过系统梳理其发展脉络与现存挑战，本综述为研究者和实践者提供全面路线图，以利用此类智能体实现更高效、可靠且符合伦理规范的科学发现。