having-model-score-confidence-step-step-detects-failures-multi-step CLM-0304observationsingle paperReviewed by AI
Having a model score its confidence step by step detects failures in multi-step reasoning better than one holistic confidence score for the whole answer, with up to 15% higher AUROC on two multi-step benchmarks, so where a chain fails is more visible per step than in the final answer.
Capability: Carrying a chain of inference through to its conclusion
Observed on
Two multi-step benchmarks, self-evaluating LLM-scorer setup, 2025; AUROC for error detection..
Sources
- supportsSelf-Evaluating LLMs for Multi-Step Tasks: Stepwise Confidence Estimation for Failure DetectionValidated by: AUROC of failure detection, stepwise versus holistic, on two multi-step benchmarks; relative gains up to 15%.. Small study (two benchmarks); the direction agrees with the process-reward literature.
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Related claims
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- When a long chain of thought is verified by classifying a graph of its steps, the predictive signal comes almost entirely from the semantic content of the steps, not the graph shape: replacing step embeddings with purely structural metadata (child/parent counts, chain index, position) drops accuracy to near chance on math, science and multiple-choice reasoning, while remaining above chance only on code.Carrying a chain of inference through to its conclusion · reviewed by AI
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- For LLM agents whose steps condition on their own prior outputs, per-step confidence that is calibrated against step-local success labels does not compose into a calibrated estimate of whether the whole trajectory succeeds, because correlated step errors make the product of step marginals diverge from joint success probability, and the divergence grows as prefix reliability falls over longer horizons.Saying it does not know · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Tasks where the error lies in the step decomposition itself, so every step looks locally fine.