arXiv AI July 7, 2026 By Chengzhen Yu, Canran Xiao, Siyuan Ma, Yang Liu

Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning

arXiv:2607. 03143v1 Announce Type: cross Abstract: Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making similarity brittle and overly confident under paraphrase and omitted details.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 22

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model

arXiv:2607. 18958v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks.

By Sibo Wang, Jie Zhang, Shiguang Shan, Xilin Chen, Wen Gao

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 17

Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model

arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.

By Jinghan Wu, Jing Li, Ivor W. Tsang, Xuetao Zhang

multimodal benchmarks safety

arXiv AI

Jun 8

TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment

arXiv:2606. 07451v1 Announce Type: cross Abstract: Vision-language models such as CLIP are highly useful for diverse tasks due to their shared image-text embedding space.

By Sweta Mahajan, Sukrut Rao, Jiahao Xie, Alexander Koller, Bernt Schiele

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jul 2

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

arXiv:2607. 00858v1 Announce Type: cross Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations.

By Peiyuan Zhu, Shaoan Xie, Zijian Li, Yifan Shen, Namrata Deka, Harsh Shrivastava, Guangyi Chen, Kun Zhang

multimodal safety

Hugging Face Trending Papers

Jul 21

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model

While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks. Existing defense methods predominantly target single-task scenarios (e.

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 10

Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction

arXiv:2606. 10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness.

By Lijia Yu, Jiuxin Cao, Yuchen Qiang, Changhao Chen, Yifei Huang, Bo Liu

llms multimodal safety

Related stories

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model

Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model

TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model

Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction