TTCL jointly improves LLM reasoning accuracy and confidence calibration on unlabeled target data, with +40.13% average relative accuracy and +70.80% ECE reduction on base models
The work proposes Test-Time Calibration Learning (TTCL), a label-free framework that derives self-supervision signals for correctness and calibration from multiple model-generated responses and jointly adapts reasoning accuracy and verbalized confidence on unlabeled target-task data, reporting consistent improvements in both accuracy and calibration on mathematical reasoning and factual question answering.