arXiv AI
Jun 9
Multilingual Training and Evaluation Resources for Vision-Language Models
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
Vision-Language Models are Fragile Multilingual Associators
arXiv:2608. 12333v1 Announce Type: cross Abstract: Vision-language models must associate visual entities with textual attributes.
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arXiv AI
Jun 19
The Hidden Evolution of Disguised Visual Context inside the VLM
arXiv:2606. 20077v1 Announce Type: cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals.
arXiv Machine Learning
Jun 8
The Dual Mechanisms of Spatial Variable Binding in Vision-Language Models
arXiv:2603. 22278v2 Announce Type: replace-cross Abstract: Many multimodal tasks, such as image captioning and visual question answering, require vision-language models (VLMs) to bind objects with their properties and spatial relations.
arXiv AI
Jun 19
ROSE: Benchmarking the Perception-to-Action Gap in Multimodal Models
arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.
arXiv Machine Learning
Jul 7
Pathways of Visual Information Flow in Vision-Language Models
arXiv:2607. 03358v1 Announce Type: cross Abstract: We study how visual information is routed in vision-language models (VLMs).
arXiv AI
Aug 12
On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation
arXiv:2608. 11002v1 Announce Type: cross Abstract: Text-to-image (T2I) generation has achieved remarkable progress in recent years.
arXiv Machine Learning
Jun 16
Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective
arXiv:2606. 14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge.
arXiv AI
Jun 24
Listening makes Vision Clear for VLMs
arXiv:2606. 23763v1 Announce Type: cross Abstract: Recent work typically assesses vision--language consistency using attention distributions of answer-side tokens.
arXiv AI
1d ago
jina-vlm: Small Multilingual Vision Language Model
arXiv:2512. 04032v4 Announce Type: replace-cross Abstract: We present jina-vlm, a token-efficient 2.
arXiv Machine Learning
Jun 8
Diagnosing Visual Ignorance in Vision-Language Models
arXiv:2606. 06890v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) frequently rely on language priors, producing confident answers that are weakly grounded in visual evidence.
arXiv Machine Learning
Jun 3
Visual Instruction Tuning Aligns Modalities through Abstraction
arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.
arXiv AI
Jun 2
VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.