---
id: 20260816-T0-09
title: "观点：推理是可学习的基于规则的过程，而非概率生成"
title_en: "Position Paper: Reasoning Is a Learnable Rule-Based Process, Not Probabilistic Generation"
url: https://ai.daily.yangsir.net/daily/20260816-T0-09
issue_date: 2026-08-16
publish_date: 2026-08-15T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2608.12325
---

# 观点：推理是可学习的基于规则的过程，而非概率生成

arXiv一篇Position论文提出，自主推理应被理解为可学习的基于规则的过程，而非当前主流深度概率生成模型所隐含的归纳。作者认为，推理的历史根基在符号AI，神经模型的成功不应掩盖这一本质。该观点挑战当前LLM推理研究的方向，可能引导后续工作更注重规则学习与神经架构的结合。

## English Version

**Position Paper: Reasoning Is a Learnable Rule-Based Process, Not Probabilistic Generation**

A new arXiv position paper argues autonomous reasoning should be understood as a learnable rule-based process, not the inductive process implied by deep probabilistic generative models. The authors note reasoning's roots in symbolic AI and argue neural successes shouldn't obscure this essence. The view challenges current LLM reasoning research directions, potentially steering future work toward hybrid rule-learning approaches.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2608.12325)

**详情页**：https://ai.daily.yangsir.net/daily/20260816-T0-09

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