Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)

Failed reasoning traces encode recoverability structure for test-time routing and post-training analysis

A new arXiv paper argues that failed reasoning traces from post-trained LLMs contain exploitable signal about whether failures are recoverable via resampling or require structural intervention. The authors derive three trajectory features from the distributional signature of failed rollouts (not their text content) that cluster failures into stable regimes and characterize failure topography across post-training methods with 84.3% accuracy. A training-free routing rule built on these features lifts rescue rates by +12.2% on a deployment-relevant hard subset, and the features transfer across model families. The work reframes failed traces as diagnostic objects rather than discarded data, with implications for inference-time compute allocation and post-training analysis.