arXiv AI
Seeing Depth but Choosing Cues in Vision-Language Models
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arXiv Machine Learning
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Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?
arXiv:2605. 20448v2 Announce Type: replace-cross Abstract: Vision-language models reliably name objects in a scene, but do they represent the 3D layout those objects inhabit?
arXiv Computation and Language
Sep 2
Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts
arXiv:2609.00293v1 Announce Type: new Abstract: We investigate how vision-language models (VLMs) handle context-memory conflicts; that is, situations in which the model is given information in contex...
arXiv Computer Vision
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Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs
Vision‑language models (VLMs) can lose accuracy when images are resized, even with minimal changes. The study shows that such small visual configuration changes—like tiling or token arrangement—cause more correctness flips across multiple checkpoints and benchmarks. Interestingly, in many cases the models still read the correct answer but fail to use it, and attention interventions reveal that configuration shifts weaken the use of readable information. By guiding models with field cues and their own transcriptions, the authors correct 97.2% of these errors.
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 Computer Vision
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Composition, Not Conversation: VLMs Lose the Scene, Not the Thread
arXiv:2609.38368v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA bench...
arXiv AI
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Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models
arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.