SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

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 fe

Read original source ↗ Open in workspace

recordType

paper

region

Global