Position: Reasoning is a Learnable Rule-Based Process
Position: Reasoning is a Learnable Rule-Based Process
观点：推理是一个可学习的基于规则的过程
Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent advances have mainly emerged from deep probabilistic generative models. Despite immense interest and rapid progress, the generative AI community has not clearly converged on operational definitions for reasoning and often implicitly rejects the historical treatment of this topic in logic and verifiable automated reasoning.
摘要： 自主推理是当今人工智能领域最具科学和经济驱动力的课题之一。从历史上看，这曾是符号人工智能的范畴，但近期的进展主要源于深度概率生成模型。尽管人们对此兴趣浓厚且进展迅速，但生成式人工智能社区尚未就推理的操作性定义达成明确共识，且往往隐含地排斥了逻辑学和可验证自动推理领域对该课题的历史性研究。
This position contends that definitional ambiguity leaves the construct validity of reasoning evaluation unverifiable, undermining quantifiable progress toward trustworthy autonomous reasoning. We also contend that this ambiguity is addressable. To that end, we provide (1) operational definitions based on a synthesis of the literature, positioning valid and sound reasoning as a learnable rule-based process; and (2) a checklist for best practices in the communication of AI reasoning research.
本文观点认为，定义的模糊性使得推理评估的构念效度无法验证，从而削弱了在实现可信自主推理方面可量化的进展。我们同时认为，这种模糊性是可以解决的。为此，我们提供了：(1) 基于文献综述的操作性定义，将有效且合理的推理定位为一种可学习的基于规则的过程；(2) 一份关于人工智能推理研究交流的最佳实践清单。
Paper Details:
- Authors: Rachel Lawrence, Jacqueline Maasch
- Journal Reference: Proceedings of the 43rd International Conference on Machine Learning (ICML), Seoul, South Korea. PMLR 306, 2026.
- DOI: https://doi.org/10.48550/arXiv.2608.12325
论文详情：
- 作者： Rachel Lawrence, Jacqueline Maasch
- 期刊引用： 第43届国际机器学习会议 (ICML) 会议录，韩国首尔。PMLR 306, 2026。
- 数字对象标识符 (DOI)： https://doi.org/10.48550/arXiv.2608.12325