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#machine learning Preprint Sep 2026

Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning

Test-time prompt tuning (TPT) enables adaptation on a single test instance, achieving improved accuracy but often sacrificing calibration performance. Most existing calibration methods introduce additional regularization terms to promote dispersion across text embeddings and reduce calibration error, yet these methods...

Yu-Wei Liang, Jian Liang, Dapeng Hu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

DualStake: Dual-Path Confidence Calibration in Deep Research Agents

It is found that Evidence Confidence (E-Conf), elicited after the final retrieval step, provides a stronger uncertainty signal than Answer Confidence (A-Conf), elicited after answer generation, and that A-Conf is largely shaped by E-Conf.

Yi-Nuo Xu, Yuanqing Liang, Jianjie Cheng et al. · 0 citations

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