构建能自主开展长期科研的AI系统，推动科学发现范式变革。
Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges
- 聚焦科学任务的AI代理需整合推理、证明与建模能力。
- 现有大模型在科学推理上取得进展但缺乏长期自主研究能力。
- 适合关注AI驱动科研自动化、跨学科融合的研究者。
科学发现是复杂认知过程，持续推动人类知识与技术进步。尽管人工智能在科学推理、模拟与实验自动化方面取得显著进展，我们仍缺乏能够自主开展长期科学研究与发现的集成化AI系统。本文综述当前AI在科学发现中的发展状况，重点分析大语言模型及其他AI技术在科学任务中的应用进展。随后提出关键挑战与有前景的研究方向，包括构建面向科学的AI代理、改进评估基准与指标、发展多模态科学表征，以及建立融合推理、定理证明与数据驱动建模的统一框架。解决这些挑战有望催生变革性AI工具，加速各学科领域的科学发现进程。
原文摘要 · Abstract (English)
Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery, including the need for science-focused AI agents, improved benchmarks and evaluation metrics, multimodal scientific representations, and unified frameworks combining reasoning, theorem proving, and data-driven modeling. Addressing these challenges could lead to transformative AI tools to accelerate progress across disciplines towards scientific discovery.
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