ActionLens: Diagnosing Spatial-Temporal Binding Failures in Vision-Language Models

arXiv:2609.34547 · cs.CV, cs.AI, cs.CL · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.AI, cs.CL

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Actor — 72× in this paper · explained in 3 episodes
• Accuracy — 56× in this paper · explained in 1 episode
• Benchmark — 35× in this paper · explained in 2 episodes
• Grounding — 14× in this paper · explained in 3 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Vision-language models — 5× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Gemma 3 Technical Report
• LLaVA-OneVision: Easy Visual Task Transfer
• TempCompass: Do Video LLMs Really Understand Videos?
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
• LLaVA-Video: Video Instruction Tuning With Synthetic Data
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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