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On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models
Shuoyuan Wang, Beier Luo, Hao Zeng +5
Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a promising way to optimize ca…
Occupancy-based Quantile Risk Control
Zihao Shi, Huajun Xi, Bingyi Jing +1
Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, qu…
Tail-Aware Top- On-Policy Distillation
Huipeng Huang, Hongxin Wei
On-policy distillation (OPD) has emerged as an effective paradigm for transferring knowledge between language models, where a student is trained to align its next-token distributio…
Robust Conformalized Selection with Noisy Responses
Chengyao Yu, Hongxin Wei, Bingyi Jing
Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug disc…
A Regret Perspective on Online Multiple Testing
Qingyang Hao, Kongchang Zhou, Fang Kong +1
Online Multiple Testing (OMT), a fundamental pillar of sequential statistical inference, traditionally evaluates the False Discovery Rate (FDR) and statistical power in isolation,…
Improving Backward Conformal Prediction via Non-Conformity Score Transformation
Junxian Liu, Hao Zeng, Hongxin Wei
Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees. While CP yields uncontrolled pre…
Provable Model Provenance Set for Large Language Models
Xiaoqi Qiu, Hao Zeng, Zhiyu Hou +1
The growing prevalence of unauthorized model usage and misattribution has increased the need for reliable model provenance analysis. However, existing methods largely rely on heuri…
HyPAC: Cost-Efficient LLMs-Human Hybrid Annotation with PAC Error Guarantees
Hao Zeng, Huipeng Huang, Xinhao Qu +3
Data annotation often involves multiple sources with different cost-quality trade-offs, such as fast large language models (LLMs), slow reasoning models, and human experts. In this…
Distribution-informed Efficient Conformal Prediction for Full Ranking
Wenbo Liao, Huipeng Huang, Chen Jia +3
Quantifying uncertainty is critical for the safe deployment of ranking models in real-world applications. Recent work offers a rigorous solution using conformal prediction in a ful…
Conditional Performance Guarantee for Large Reasoning Models
Jianguo Huang, Hao Zeng, Bingyi Jing +2
Large reasoning models have shown strong performance through extended chain-of-thought reasoning, yet their computational cost remains significant. Probably approximately correct (…