Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores

2026-08-31

AI Summary

研究通过添加少量参数纠正大型语言模型的输出偏差，恢复其隐藏状态中的正确答案，解决模型推理失败问题。

Abstract

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.

Problem

• large language model
• reasoning task
• output bottleneck
• hidden-state probes
• sequence scoring

Innovation

• hidden-state probes
• additive correction
• output bottleneck
• reasoning tasks
• expression failures