Prefix-Safe Bayesian Belief Tracking for LLM Reasoning Reliability:Separating Calibration from Ranking
arXiv:2605.27712v1 Announce Type: new Abstract: Long reasoning traces need reliability estimates before final answers are known. We study prefix-conditioned eventual-success estimation, $P(y=1 \mid o_{1:t})$, using prefix-safe observations. Sequential Bayesian Belief Tracking (SBBT) calibrates observation likelihoods and recursively updates a two-state belief, providing a common tracker for scalar scores, text and self-verification markers, hidden clusters, token-pooling probes, and latent-trajectory features. Across generated open-weight traces on MATH-500, GSM8K, AIME 2025, and RIMO-N, proba
The increasing complexity of LLM reasoning chains and the demand for reliable AI outputs necessitate robust methods for real-time performance evaluation and error detection.
This research offers a method to reliably estimate the success probability of long LLM reasoning traces before final answers are known, fundamentally improving the trustworthiness and deployability of advanced AI systems.
The ability to provide prefix-safe reliability estimates allows for more robust AI agent design, enabling dynamic error handling and improved autonomous execution in complex tasks.
- · AI developers
- · Autonomous agent builders
- · High-stakes AI applications
- · Software quality assurance
- · Unreliable AI systems
- · Manual error checking processes
Increased reliability metrics for LLMs will accelerate their adoption in critical applications requiring high accuracy.
The development of robust real-time error detection could lead to a new generation of self-correcting and more adaptive AI agents.
Improved AI reliability might broaden public trust in AI, potentially speeding up regulatory acceptance for autonomous systems in various sectors.
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Read at arXiv cs.AI