arXiv cs.AIPaper
Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores
This reframes a common diagnostic: when models fail reasoning tasks, they might have the capability but can't route it to the output layer cleanly. The recovery using minimal additive corrections is striking and transfers across model families. For teams building reasoning systems, this suggests looking upstream of token prediction when accuracy degrades. Could matter for understanding where to invest in training or inference-time verification.