Hugging Face Trending Papers August 19, 2026

Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?

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arXiv AI

Aug 21

Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?

arXiv:2608. 19376v1 Announce Type: cross Abstract: Split-conformal prediction provides marginal coverage under exchangeability and is increasingly used as an abstention layer for zero-shot vision-language models (VLMs).

By Jai Kumar Sharma, Amartya Dutta

llms multimodal safety

arXiv Machine Learning

Jul 21

The Label Complexity of Class-Conditional Coverage under Distribution Shift

arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.

By Weijia Han, Lisha Qu

benchmarks

arXiv Machine Learning

Aug 3

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

arXiv:2607. 28696v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class.

By Mushir Akhtar, M. Tanveer

llms multimodal

arXiv Computer Vision

Sep 1

GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models

arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...

By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e

llms multimodal benchmarks

arXiv Computer Vision

Sep 10

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

arXiv:2609.10333v1 Announce Type: new Abstract: Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-fr...

By Xuan Cuong Ngo, Ngan Le

llms fine-tuning multimodal safety

arXiv AI

Jun 10

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks

arXiv:2606. 10066v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years, yet reported accuracy assumes these examples were absent from pretraining.

By Bruce Changlong Xu, Lan Wu, Alexander Ryu

llms multimodal benchmarks

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Related stories

Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?

The Label Complexity of Class-Conditional Coverage under Distribution Shift

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks