这篇论文揭示了语言模型内部逻辑表示与行为表达的分离现象，对理解LLM推理机制有重要价值。
研究者在五个开源transformer模型中测试逻辑验证能力，使用有效-无效前提-声明对。尽管行为表现接近随机水平，逻辑有效性仍可从隐藏状态中完美解码。在保留模板、领域和推理家族条件下，有效性仍保持强可解码性。在行为不正确但评估条件明确的例子中，有效性仍高度可解码。
When Decodability Is Not Enough: Logical Validity Representations, Behavioral Dissociation, and Causal Tests in Language Models
Large language models can look capable of logical reasoning, but correct or incorrect answers alone tell us little about what the model represents internally. We study logical verification in five open-weight transformer models using matched valid--invalid premise--claim pairs that vary across inference families, semantic domains, templates, and difficulty levels. Despite near-chance behavioral performance, logical validity is often almost perfectly decodable from hidden states and remains strongly decodable under held-out templates, domains, and inference families. Validity also remains highly decodable on behaviorally incorrect examples in the conditions where correctness-conditioned evaluation is well defined. At the same time, exhaustive leave-one-out tests reveal clear limits to this generalization, and interventions along probe-derived validity directions have only weak, nonspecific effects compared with random controls. Our results suggest that representing validity, expressing it in behavior, and using it causally are distinct. Validity related information can be strongly decodable from a model's hidden states without being reliably expressed in its output.