Visual Attention Faithfulness in Vision-Language Models is Heterogeneous

arXiv:2609.00830 · cs.CV, cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Terminology

• Ablation — 12× in this paper · explained in 1 episode
• Transformer — 11× in this paper · explained in 2 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Natural Language Processing — 3× in this paper
• Redundancy — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Generalizability — 2× in this paper · explained in 6 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Instance Segmentation — 2× in this paper · explained in 3 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Activation Patching — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen2.5-VL Technical Report
• How to use and interpret activation patching
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

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