综述大模型如何辅助科研全流程，从选题到评审。
Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
- 系统梳理大模型在科研五环节的应用：文献检索、创意生成、实验设计、内容写作、成果评价。
- 覆盖方法、数据集、评估方式与伦理风险，含生成模型滥用对研究诚信的威胁。
- 适合想了解AI+科学的初学者与推动未来智能科研系统的开发者。
随着大型多模态语言模型的出现，科学正面临基于人工智能的技术变革。一个新兴的模型与工具生态系统旨在支持研究人员贯穿整个科研生命周期，包括（1）查找相关文献，（2）生成研究思路并开展实验，（3）撰写文本内容，（4）创建图表等多模态成果，以及（5）评估科研工作，如同行评审。本文综述了代表性的核心技术、评估实践与新兴趋势，涵盖上述五个任务中的数据集、方法、结果、评估策略、局限性及伦理问题，特别关注生成模型误用对科研诚信的风险。本综述旨在为领域新人提供清晰的入门指引，并激发新的基于AI的科研倡议，推动其融入未来的「AI for Science」系统中。
原文摘要 · Abstract (English)
With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. An emerging ecosystem of models and tools aims to support researchers throughout the scientific lifecycle, including (1) searching for relevant literature, (2) generating research ideas and conducting experiments, (3) producing text-based content, (4) creating multimodal artifacts such as figures and diagrams, and (5) evaluating scientific work, as in peer review. In this survey, we provide a curated overview of literature representative of the core techniques, evaluation practices, and emerging trends in AI-assisted scientific discovery. Across the five tasks outlined above, we discuss datasets, methods, results, evaluation strategies, limitations, and ethical concerns, including risks to research integrity through the misuse of generative models. We aim for this survey to serve both as an accessible, structured orientation for newcomers to the field, as well as a catalyst for new AI-based initiatives and their integration into future ``AI4Science'' systems.
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