Dynamic or problem-specific certainty thresholds for CGR

Determine whether dynamically chosen or problem-specific certainty thresholds for early stopping in Certainty-Guided Reasoning (CGR) improve accuracy and computational efficiency compared to a fixed threshold (e.g., 0.97), including calibration strategies such as online adaptation and the use of external signals like input complexity.

Background

The paper introduces Certainty-Guided Reasoning (CGR), which halts reasoning once a model’s internal confidence (certainty) exceeds a fixed threshold, empirically set to 0.97. While effective, a single global threshold may be suboptimal across heterogeneous problems.

The authors suggest that thresholds could be tailored per-problem or adapted online using signals such as input complexity, which raises the question of whether dynamic thresholding yields better performance and efficiency than a fixed setting.

References

Several promising directions remain open for exploration. First, although we used a fixed certainty threshold across all problems, dynamic or problem-specific thresholding may yield better results, especially if calibrated online or using external signals like input complexity.

— Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach  (2509.07820 - Nogueira et al., 9 Sep 2025) in Conclusions and Future Work

Our controlled replay isolates the effect of the calibration map under a fixed policy; it does not establish that a fixed $0.7$ threshold, or confidence gating alone, is optimal for a live agent.

— Evaluating Confidence-Gated Retrieval with Matched Trajectory Replay  (2608.26846 - Chhikara, 27 Aug 2026) in Section 6, Discussion and Conclusion