Quantifying and Understanding Uncertainty in Large Reasoning Models

📰 ArXiv cs.AI

Learn to quantify uncertainty in Large Reasoning Models using conformal prediction for statistically rigorous uncertainty sets

advanced Published 16 Apr 2026

Action Steps

Apply conformal prediction to Large Reasoning Models to construct uncertainty sets
Use distribution-free and model-agnostic methodologies to quantify generation uncertainty
Evaluate the performance of conformal prediction using finite-sample guarantees
Compare traditional methods with conformal prediction for uncertainty quantification
Implement conformal prediction in LRMs to improve reasoning-answer generation reliability

Who Needs to Know This

AI researchers and engineers working with Large Reasoning Models can benefit from this knowledge to improve model reliability and trustworthiness

Key Insight

💡 Conformal prediction provides statistically rigorous uncertainty sets for Large Reasoning Models

Key Takeaways

Learn to quantify uncertainty in Large Reasoning Models using conformal prediction for statistically rigorous uncertainty sets

Full Article

Title: Quantifying and Understanding Uncertainty in Large Reasoning Models

Abstract:
arXiv:2604.13395v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LRMs is crucial, traditional methods are often insufficient because they do not provide finite-sample guarantees for reasoning-answer generation. Conformal prediction (CP) stands out as a distribution-free and model-agnostic methodology that constructs statistically rigorous uncertainty sets. However, e

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