arXiv AI August 6, 2026 By Mario Leiva, Yue Ma, Qinru Qiu, Gerardo Simari, Paulo Shakarian

Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models

arXiv:2608. 04190v1 Announce Type: new Abstract: Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as majority voting trade recall for precision and are brittle to coordinated failures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 14

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification

arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.

By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig

llms diffusion

arXiv Computer Vision

Aug 25

Specialist-Generalist Fusion with Outcome-Supervised Rationales for Deepfake Detection

arXiv:2605.31192v2 Announce Type: replace Abstract: Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation trac...

By Benedikt Hopf, Zongwei Wu, Radu Timofte

robotics benchmarks safety

arXiv Computer Vision

2d ago

Subtoken Vision Transformer for Fine-grained Recognition

arXiv:2607.09086v2 Announce Type: replace Abstract: We present Subtoken Vision Transformer (SubViT), a selective image tokenization method for fine-grained visual recognition. Standard Vision Transfo...

By Jie Zhu, Ivy Zhang, Minchul Kim, Xiaoming Liu

llms computer-vision nlp

arXiv Machine Learning

Jun 4

Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC

arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.

By Haolu Liu, Xiyue Wang, Xuanting Xie, Liangjian Wen, Zhao Kang

llms benchmarks

arXiv AI

Aug 5

In-Context Collapse in Vision-Language Models and How to Mitigate it?

arXiv:2608. 02830v1 Announce Type: cross Abstract: Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied.

By Mohammad Rostami

llms computer-vision multimodal benchmarks

arXiv AI

Jul 1

The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning

arXiv:2606. 30875v1 Announce Type: cross Abstract: Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thresholds.

By Brent A. Griffin, Jason J. Corso

llms computer-vision fine-tuning efficiency multimodal benchmarks safety