Large language models have become remarkably capable at complex reasoning, but knowing when they're wrong remains a critical challenge. A promising new approach addresses this by having models evaluate their confidence not just at the end of a task, but after each individual reasoning step.

The Problem with End-to-End Evaluation

Traditional confidence estimation treats multi-step reasoning as a black box. The model completes an entire chain of thought—perhaps solving a math problem or answering a complex question—and only then assesses how confident it is in the final answer. This holistic approach has a fundamental weakness: a single error early in the reasoning chain can cascade through subsequent steps, but the model can't identify where things went wrong.

Stepwise Confidence: Catching Errors as They Happen

The stepwise approach introduces a fundamentally different paradigm. After each reasoning step, the model pauses to evaluate: "How confident am I that this step is correct?" This creates a detailed confidence profile across the entire reasoning process, rather than a single score at the end.

This granular self-assessment offers several advantages. It can pinpoint exactly where reasoning begins to falter, distinguish between uncertain conclusions based on solid reasoning versus confident conclusions built on faulty logic, and potentially enable early intervention when confidence drops below acceptable thresholds.

The Results

Empirical evaluation demonstrates the effectiveness of this approach, with stepwise confidence scoring achieving up to 15% relative improvement in AUC-ROC compared to holistic methods. This suggests that models can more accurately identify their own errors when they evaluate incrementally rather than retrospectively.

Implications for Reliable AI

As LLMs are deployed in high-stakes applications, the ability to detect failures becomes as important as raw performance. Stepwise confidence estimation represents a meaningful step toward more transparent and trustworthy AI systems—ones that not only solve problems but understand the reliability of their own reasoning process.