The study introduces a new framework for improving the reliability of reasoning in large language models (LLMs) by using Sequential Bayesian Belief Tracking (SBBT) to better estimate success probabilities. Findings suggest that while scalar scores enhance probability quality, structure-aware observations are crucial for ranking improvements, particularly in challenging mathematical contexts.