arXiv Machine Learning
Aug 11

Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning

arXiv:2608. 09011v1 Announce Type: new Abstract: Uncertainty Quantification (UQ) aims to measure the reliability of model predictions, serving as a critical safeguard for deploying Vision-Language Models (VLMs) in safety-critical scenarios.

By Ao Zhou, Zhiwei Jiang, Zifeng Cheng, Cong Wang, Shufan Yang, Haoru Chen, Qing Gu
arXiv AI
Aug 7

Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift

arXiv:2605. 16411v2 Announce Type: replace-cross Abstract: Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible yet physically inconsistent or visually ungrounded responses due to likelihood maximization under joint probabilistic modeling.

By Qinwu Xu