识别大模型推理中的三种思维链病理，提供可落地的检测工具。
Diagnosing Pathological Chain-of-Thought in Reasoning Models
- 设计三类任务无关的简易指标，区分推理故障类型。
- 通过特制模型验证，能有效识别后置合理化、编码推理等病理。
- 适合关注AI安全与模型可解释性的研究者使用。
思维链（CoT）推理是现代大模型的核心机制，也是AI安全的关键干预点。然而，CoT推理可能表现出多种失效模式，我们称之为病理：后置合理化（模型从预设答案反推看似合理的解释）、编码推理（中间步骤将信息隐藏在看似可读的文本中）、内化推理（用无意义填充符替代显式推理，内部完成计算）。为更好理解并区分这些病理，我们构建了一套简单易实现、计算成本低且任务无关的量化指标。为验证方法有效性，我们开发了专门训练以呈现特定病理的模型。本工作提供了评估思维链病理的实用工具包，对训练过程中的监控具有直接应用价值。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) reasoning is fundamental to modern LLM architectures and represents a critical intervention point for AI safety. However, CoT reasoning may exhibit failure modes that we note as pathologies, which prevent it from being useful for monitoring. Prior work has identified three distinct pathologies: post-hoc rationalization, where models generate plausible explanations backwards from predetermined answers; encoded reasoning, where intermediate steps conceal information within seemingly interpretable text; and internalized reasoning, where models replace explicit reasoning with meaningless filler tokens while computing internally. To better understand and discriminate between these pathologies, we create a set of concrete metrics that are simple to implement, computationally inexpensive, and task-agnostic. To validate our approach, we develop model organisms deliberately trained to exhibit specific CoT pathologies. Our work provides a practical toolkit for assessing CoT pathologies, with direct implications for training-time monitoring.
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