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.
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.
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arXiv AI
Sep 16
Same Answer, Different Representations: Hidden instability in VLMs
arXiv:2602.06652v2 Announce Type: replace Abstract: The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions r...
arXiv Computation and Language
Sep 22
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-la...
arXiv Computer Vision
6d ago
OpenVAM: Open-World Visual Attention Modeling with VLMs
OpenVAM is a new framework for visual attention modeling that combines a dense saliency map with language‑based explanations. It uses a decoupled design: a visual pathway for precise localization and a vision‑language head that generates grounded what/why explanations. The method is trained in three stages to preserve localization while adding language grounding, and a scalable pipeline creates multi‑domain annotations for evaluation.
arXiv Computation and Language
Aug 25
Knowing Isn't Always Saying: When Do Spatial Encodings Reach Answers in Vision-Language Models?
arXiv:2608.22916v1 Announce Type: new Abstract: Vision-language models are known to encode spatial information in their hidden states, yet often fail to use it when answering. However, it remains unc...
arXiv AI
Jun 8
Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models
arXiv:2605. 20950v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference.
arXiv AI
Jun 3
When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics
arXiv:2606. 03569v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference.
Hugging Face Trending Papers
Jul 22
Test-Time Training for Modality Order Consistency in Vision-Language Models
We find that vision-language models are sensitive to a specific semantically irrelevant change: the order in which the image and question are presented. Across three models and three benchmarks, image first prompting consistently outperforms question-first prompting, revealing a repeatable modality order failure.
arXiv AI
Jul 28
Disentangling Semantic Attention from Structural Bias in the Attention Manifold
arXiv:2607. 24017v1 Announce Type: cross Abstract: The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws.
Hugging Face Trending Papers
Jun 2
When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics
Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference. While visual token pruning offers a promising solution, existing methods predominantly rely on initial attention scores.
arXiv Computation and Language
Aug 25
Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression
arXiv:2606.14782v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with lo...
arXiv AI
Jul 15
Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models
arXiv:2509. 22415v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved strong vision-language performance, yet their token-level visual evidence remains difficult to inspect.