Reasoning or Fluency? Dissecting Probabilistic Confidence in Best-of-N Selection

TL;DR

This paper critically examines whether probabilistic confidence metrics truly reflect reasoning quality in Best-of-N selection, revealing they mainly measure fluency rather than logical dependencies, and proposes a causality-based alternative.

Contribution

The work demonstrates the insensitivity of current confidence metrics to reasoning structure and introduces a contrastive causality metric for more accurate reasoning assessment.

Findings

Probabilistic confidence metrics are largely insensitive to inter-step causal disruptions.

Model performance remains stable despite severe causality perturbations.

A new causality-based metric improves the faithfulness of reasoning evaluation.

Abstract

Probabilistic confidence metrics are increasingly adopted as proxies for reasoning quality in Best-of-N selection, under the assumption that higher confidence reflects higher reasoning fidelity. In this work, we challenge this assumption by investigating whether these metrics truly capture inter-step causal dependencies necessary for valid reasoning. We introduce three classes of inter-step causality perturbations that systematically disrupt dependencies between reasoning steps while preserving local fluency. Surprisingly, across diverse model families and reasoning benchmarks, we find that selection accuracy degrades only marginally under these disruptions. Even severe interventions, such as applying hard attention masks that directly prevent the model from attending to prior reasoning steps, do not substantially reduce selection performance. These findings provide strong evidence that current probabilistic metrics are largely insensitive to logical structure, and primarily capture surface-level fluency or in-distribution priors instead. Motivated by this gap, we propose a contrastive causality metric that explicitly isolates inter-step causal dependencies, and demonstrate that it yields more faithful output selection than existing probability-based approaches.