Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

• URL: http://arxiv.org/abs/2605.19228v1
• Date: Tue, 19 May 2026 00:57:51 GMT
• Title: Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
• Abstract summary: We introduce Stepwise Confidence Attribution (SCA), a framework for closed-source LLMs that assigns step-level confidence based only on generated reasoning traces.<n>SCA reliably identifies low-confidence steps strongly correlated with reasoning errors.<n>Using step-level confidence to guide self-correction improves the correction success rate by up to 13.5% over answer-level feedback.
• Score: 12.955460962298588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing where a multi-step reasoning trace might fail remains difficult. Confidence estimation offers a diagnostic signal, yet existing methods are restricted to final answers or require internal model access. In this paper, we introduce Stepwise Confidence Attribution (SCA), a framework for closed-source LLMs that assigns step-level confidence based only on generated reasoning traces. SCA applies the Information Bottleneck principle: steps aligning with consensus structures across correct solutions receive high confidence, while deviations are flagged as potentially erroneous. We propose two complementary methods: (1) NIBS, a non-parametric IB approach measuring consistency without graph structures, and (2) GIBS, a graph-based IB model that learns subgraphs through a differentiable mask to capture logical variability. Extensive experiments on mathematical reasoning and multi-hop question answering show that SCA reliably identifies low-confidence steps strongly correlated with reasoning errors. Moreover, using step-level confidence to guide self-correction improves the correction success rate by up to 13.5\% over answer-level feedback.

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