给推理下定义，让AI推理可验证可学习
Position: Reasoning is a Learnable Rule-Based Process
- 将推理定义为可学习的规则化过程，统一理论基础
- 提出评估推理能力的可操作标准与检查清单
- 适合关注AI可解释性与可信推理的研究者
自主推理是当今人工智能领域最具科学与经济价值的主题之一。历史上属于符号主义AI范畴，近年来主要由深度概率生成模型推动发展。尽管兴趣浓厚、进展迅速，生成式AI领域尚未就推理的明确定义达成共识，且常隐性排斥逻辑与可验证自动推理的传统方法。本文认为，定义模糊导致推理评估的建构效度无法验证，阻碍了可量化可信推理的发展。为此，本文提出：(1) 基于文献综述的操作性定义，将有效且严谨的推理定位为可学习的规则化过程；(2) 提供一套最佳实践沟通检查清单，以提升AI推理研究的透明度与可复现性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。