# 形式化潜在思想：LLM思维表示的四条公理

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-07 08:00
- AIHOT 分数：37
- AIHOT 链接：https://aihot.virxact.com/items/cmqylfgci02xvsliv7uolrty9
- 原文链接：https://arxiv.org/abs/2606.27378

## AI 摘要

一项研究提出评估LLM潜在思想表示的公理化框架，包含四个独立于下游benchmark的功能公理：因果性、最小性、可分离性与稳定性。在23项推理任务（如空间推理、事实问答）上审计多款开源权重LLM后发现：没有模型能同时满足所有公理；表示可区分任务类型，但无法区分同一任务内的不同问题；表示编码的信息几乎不超出输入嵌入本身。该缺陷在密集、推理蒸馏和RL训练的模型家族中一致出现，表明其是结构性而非模型规模或训练方法的属性。

## 正文

We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal representational failures that benchmark accuracy masks. Existing evaluations conflate representation quality with model capacity. Therefore, failures cannot be attributed to the representation rather than to the model that processes it. We formalize four functional axioms (Causality, Minimality, Separability, and Stability) and define a quantitative measure for each, computed directly on the representation independently of downstream accuracy. We audit open-weight LLMs across 23 reasoning tasks (e.g., Spatial Reasoning, Factual QA). We find that no candidate satisfies all four axioms simultaneously, that the representations distinguish task type reliably but cannot distinguish between two questions within the same task, and that the representations encode little information beyond what is already present in the input embedding. The failure is consistent across dense, reasoning-distilled, and RL-trained model families, indicating that the gap is structural rather than a property of model size or training procedure.