arXiv Computer Vision
VisLens: Single-Pass Interpretable Visual Search for Multimodal LLMs
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Visual Access Boundaries in Vision-Language Model Reasoning
arXiv:2607. 12815v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extended when VLMs generate longer reasoning traces.
Hugging Face Trending Papers
Jul 14
Visual Access Boundaries in Vision-Language Model Reasoning
Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extended when VLMs generate longer reasoning traces. We ask whether CoT requires continued access to image tokens, or whether it mainly operates over visual information already made available earlier in the forward pass.
Hugging Face Trending Papers
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Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
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arXiv Computer Vision
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Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
The paper introduces Visual Retrieval Heads (VRHs), a small fraction of attention heads in vision‑language models that are causally responsible for grounding text descriptions to image regions. By recasting head‑scoring methods and evaluating across eleven VLMs and five benchmarks, the authors show that masking the top 20 VRHs can drop grounding accuracy by up to 80 percentage points, while random masking has little effect. VRHs generalize across various visual reference tasks, preserve output format while corrupting localization, and transfer causally across models sharing an LLM backbone.
arXiv AI
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HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling
arXiv:2510. 00054v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding tasks.
arXiv AI
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Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.