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.
Same Answer, Different Representations: Hidden instability in VLMs
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arXiv AI
Jun 17
Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models
arXiv:2606. 17389v1 Announce Type: cross Abstract: Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical.
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 Machine Learning
Jul 13
The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs
arXiv:2607. 09544v1 Announce Type: cross Abstract: Despite strong performance on many multimodal tasks, vision-language models (VLMs) still struggle with basic object counting.
arXiv Computation and Language
Sep 2
Reliability Challenges in Diffusion Vision-Language Models
The paper presents the first systematic reliability evaluation of diffusion-based Large Vision‑Language Models (dLVLMs), comparing six diffusion models to autoregressive (AR) baselines across four dimensions. Key findings include a reversal of the yes‑bias seen in AR models for binary visual queries, competitive hallucination rates but lower linguistic quality, near‑zero accuracy for underrepresented racial groups with opposite‑polarity gender bias, and accuracy collapse in multiple‑choice tasks when the correct option is shorter than distractors due to a length prior emerging at the first denoising step. Additionally, tokens committed late in denoising with low confidence correlate with hallucinated content, indicating a unique mechanistic signal in diffusion generation.
arXiv AI
Jun 9
The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models
arXiv:2606. 07861v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored.
arXiv Computer Vision
Sep 7
FailSAE: Towards Interpretable Failure Prediction for Vision-Language Models via Sparse Autoencoders
The paper introduces FailSAE, a method that uses Sparse Autoencoders to predict failures in vision‑language models (VLMs) such as CLIP. By treating failure prediction as a classification over sparse SAE latent activations and employing a three‑stage training pipeline, the approach yields higher prediction accuracy than existing confidence‑score or auxiliary‑classifier baselines. Analysis shows that the SAE captures class‑specific concepts and reveals a shift toward ambiguous or style‑related concepts during failures, offering insights for runtime failure recovery.
arXiv AI
Aug 14
Vision-Language Models are Fragile Multilingual Associators
arXiv:2608. 12333v1 Announce Type: cross Abstract: Vision-language models must associate visual entities with textual attributes.
Hugging Face Trending Papers
Aug 3
Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning
Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual instruction tuning, internal MLLM representations rapidly deviate from their original semantic states during inference, causing severe information degradation.
arXiv Machine Learning
Jul 9
Unveiling the Visual Counting Bottleneck in Vision-Language Models
arXiv:2605. 30170v2 Announce Type: replace-cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting.
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 AI
Aug 10
Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving
arXiv:2603. 06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios.