发现大模型推理失败不等于能力缺失，用简单方法就能恢复被掩盖的内部逻辑。
研究人员发现大语言模型在推理任务失败时，可能并非缺乏底层能力，而是输出阶段存在瓶颈。通过隐藏状态探测，即使在序列评分完全崩溃的情况下，仍能解码出正确答案。使用最小化无标签校正协议，仅用25个未标记样本和2个参数，就能为Qwen3.5模型恢复9-34个准确点，该方法成功迁移到OLMo-2-1B和Llama-3.1-8B模型。
Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores
When a large language model fails a reasoning task, it is often assumed to lack the underlying capability. However, this conflates a genuine absence of reasoning with a late-stage output bottleneck. We observe a consistent readout gap across diverse reasoning benchmarks: hidden-state probes successfully decode correct answers even when native sequence scoring completely collapses due to structural biases. To test whether instance-specific logic survives this collapse, we introduce a diagnostic protocol using a minimal, target-label-free additive correction. Fitting just two parameters on as few as 25 unlabeled examples recovers 9--34 accuracy points for Qwen3.5 models, transferring successfully to OLMo-2-1B and Llama-3.1-8B. Crucially, these recovered decisions persist on hard instances unresolved by simple lexical overlap and significantly exceed count-preserving permutation baselines. Our results show that many apparent zero-shot reasoning deficits are expression failures masking intact internal logic, urging a narrower interpretation of benchmark evaluations.