Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning
Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference. We formulate this choice as a compute-aware three-action controller, scoring terminal actions by predicted answer success and expansion by a rollout-based continuation-success surrogate, with compute cost priced explicitly. A local action-gap analysis characterizes when score errors can change a decision, while an expected regret bound accounts for errors along the realized expansion path. Guided by this analysis, we learn and calibrate action-specific confidence estimates, with local uncertainty penalties for ambiguous decisions. On held-out wid...
| Authors | Weida Liang, Kenji Kawaguchi |
|---|---|
| Primary category | cs.AI |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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