AI能快速生成科学假说，但必须验证才能推动真实发现。
The Need for Verification in AI-Driven Scientific Discovery
- 用机器学习和大语言模型生成海量假说
- 现有方法缺乏可靠验证机制，可能阻碍科学进步
- 强调验证是AI辅助发现的核心，适合科研人员参考
人工智能正在重塑科学研究方式。机器学习与大型语言模型（LLMs）可以远超传统方法的速度和规模生成科学假说，有望加速跨领域的发现进程。然而，假说数量的激增也带来关键挑战：若缺乏可扩展且可靠的验证机制，科学进展反而可能受阻。本文追溯科学发现的历史演进，分析AI如何改变既有研究范式，并综述从数据驱动方法、知识感知神经架构到符号推理框架及LLM代理等主要技术路径。尽管这些系统能识别模式并提出候选定律，其科学价值最终取决于严谨透明的验证过程，我们认为验证应成为人工智能辅助发现的基石。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is transforming the practice of science. Machine learning and large language models (LLMs) can generate hypotheses at a scale and speed far exceeding traditional methods, offering the potential to accelerate discovery across diverse fields. However, the abundance of hypotheses introduces a critical challenge: without scalable and reliable mechanisms for verification, scientific progress risks being hindered rather than being advanced. In this article, we trace the historical development of scientific discovery, examine how AI is reshaping established practices for scientific discovery, and review the principal approaches, ranging from data-driven methods and knowledge-aware neural architectures to symbolic reasoning frameworks and LLM agents. While these systems can uncover patterns and propose candidate laws, their scientific value ultimately depends on rigorous and transparent verification, which we argue must be the cornerstone of AI-assisted discovery.
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