探索大模型如何从推理转向发现新知识，推动人工智能向真正创新迈进。
From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models
- 基于皮尔士的三重推理框架，系统梳理大模型假设发现方法。
- 揭示大模型在生成、应用与验证新假设方面的关键进展与瓶颈。
- 适合关注AI创新机制、科学发现自动化研究者阅读。
自大型语言模型（LLMs）问世以来，研究主要聚焦于提升其指令遵循与演绎推理能力，但其是否能真正发现新知识仍存疑问。为实现通用人工智能（AGI），亟需模型不仅执行指令或检索信息，更能通过提出新颖假设与理论来学习、推理并生成新知识，从而深化对世界的理解。本文基于皮尔士的溯因、演绎与归纳框架，系统梳理了基于大模型的假设发现研究，涵盖假设生成、应用与验证，总结关键成果并指出核心差距。通过整合这些方向，本文揭示了大模型如何从“信息执行者”演变为真正的创新引擎，可能重塑科研、科学及现实问题解决范式。
原文摘要 · Abstract (English)
Since the advent of Large Language Models (LLMs), efforts have largely focused on improving their instruction-following and deductive reasoning abilities, leaving open the question of whether these models can truly discover new knowledge. In pursuit of artificial general intelligence (AGI), there is a growing need for models that not only execute commands or retrieve information but also learn, reason, and generate new knowledge by formulating novel hypotheses and theories that deepen our understanding of the world. Guided by Peirce's framework of abduction, deduction, and induction, this survey offers a structured lens to examine LLM-based hypothesis discovery. We synthesize existing work in hypothesis generation, application, and validation, identifying both key achievements and critical gaps. By unifying these threads, we illuminate how LLMs might evolve from mere ``information executors'' into engines of genuine innovation, potentially transforming research, science, and real-world problem solving.
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