arXiv:2601.13735v2 Announce Type: replace 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 mode
The proliferation of complex AI models and the increasing reliance on best-of-N selection for output optimization necessitate a deeper understanding of probabilistic confidence metrics.
This research challenges a fundamental assumption in AI development, highlighting that perceived 'reasoning' in models may often be superficial 'fluency', which impacts reliability and trust.
Developers and researchers will need to re-evaluate how they interpret and utilize probabilistic confidence, potentially shifting towards more robust and interpretable metrics for AI reasoning.
- · AI safety researchers
- · Developers of robust AI evaluation methods
- · Explainable AI (XAI) frameworks
- · Developers relying solely on probabilistic confidence for reasoning validation
- · Applications where trust in AI reasoning is paramount but not rigorously verifie
AI models will be re-evaluated for their true reasoning capabilities versus mere linguistic fluency.
New evaluation benchmarks and methodologies will emerge focusing on inter-step causal dependencies.
Increased skepticism and scrutiny of AI 'intelligence' claims, leading to more grounded expectations for autonomous systems.
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Read at arXiv cs.AI