SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio

2026-03-24 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

SelfDoubt is a novel single-pass uncertainty quantification framework for reasoning Large Language Models (LLMs) that addresses the computational expense of sampling-based methods and the inconsistency of single-pass proxies. It operates by extracting behavioral signals directly from the LLM's reasoning trace, particularly through the Hedge-to-Verify Ratio (HVR). HVR quantifies the presence of uncertainty markers ("hedging") relative to explicit self-checking behavior ("verification"). The framework is suitable for proprietary APIs that do not expose logits or intermediate token probabilities. Evaluated across seven models and three multi-step reasoning benchmarks (BBH, GPQA-Diamond, MMLU-Pro), SelfDoubt demonstrates that traces with zero hedging markers are correct 96.1% of the time, offering a high-precision confidence gate at no additional cost. The full SelfDoubt score, which fuses HVR with verbalized confidence, significantly outperforms sampling-based Semantic Entropy in discrimination at 10x lower inference cost. A two-stage deployment cascade achieves 90% accuracy at 71% coverage without task-specific labels.

Key takeaway

For AI Engineers or AI Architects deploying LLMs in cost- or latency-sensitive environments, SelfDoubt offers a production-ready solution for uncertainty quantification. Its ability to operate on single reasoning traces from proprietary APIs, combined with its high precision and significantly lower inference cost compared to sampling-based methods, means you can implement robust deferral policies without extensive computational overhead or access to model internals. Consider integrating the two-stage cascade to achieve high accuracy and coverage in your LLM applications.

Key insights

SelfDoubt quantifies LLM uncertainty from single reasoning traces using a Hedge-to-Verify Ratio and verbalized confidence.

Principles

• Behavioral signals in reasoning traces indicate uncertainty.
• Zero hedging language correlates with high correctness.
• Fusion of complementary signals improves robustness.

Method

SelfDoubt uses a two-stage data-driven pipeline for marker discovery, generating seed vocabularies from model consensus and expanding them into model-specific dictionaries from 90 unlabeled traces. HVR is calculated as hedge markers divided by verify markers plus one, then fused with verbalized confidence via z-score normalization.

In practice

• Implement an HVR=0 gate for high-precision auto-acceptance.
• Use z-score fusion of HVR and verbalized confidence.
• Calibrate with 90 unlabeled traces per model.

Topics

• SelfDoubt Framework
• Uncertainty Quantification
• Reasoning Language Models
• Hedge-to-Verify Ratio
• Proprietary API Deployment

Code references

• satwik2711/SelfDoubt

Best for: AI Engineer, NLP Engineer, AI Architect, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.