AI 中文总结
本文提出推理是可学习的基于规则的过程，针对生成式AI社区推理定义模糊问题，给出操作定义与研究交流最佳实践清单，以保障推理评估的结构效度。
AI 中文摘要
自主推理是当前AI领域最具科学和经济驱动力的课题之一，历史上属于符号AI的范畴，近期进展主要来自深度概率生成模型。尽管人们对此兴趣浓厚且进展迅速，但生成式AI社区尚未就推理的操作定义达成明确共识，往往隐含地拒绝逻辑和可验证自动推理领域对该课题的历史处理方式。本文立场认为，定义模糊性导致推理评估的结构效度无法验证，损害了可量化的、通往可信自主推理的进展。同时，该模糊性是可解决的。为此，我们提供：（1）基于文献综合的操作定义，将有效且可靠的推理定位为一个可学习的基于规则的过程；（2）AI推理研究交流的最佳实践清单。
英文摘要
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. 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.
Journal refProceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026
