Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models

arXiv:2602.11244 · cs.CV · Submitted 2026-02-11 · Read on arXiv

cs.CV

Submitted: 2026-02-11

Updated: 2026-09-16

Terminology

• Grounding — 70× in this paper · explained in 3 episodes
• Accuracy — 65× in this paper · explained in 1 episode
• Sycophancy — 64× in this paper · explained in 4 episodes
• Benchmark — 36× in this paper · explained in 2 episodes
• Hallucination — 22× in this paper · explained in 3 episodes
• Abstention — 19× in this paper · explained in 1 episode
• Visual Grounding — 12× in this paper · explained in 2 episodes
• Instruction Tuning — 10× in this paper · explained in 2 episodes
• Linear probe — 10× in this paper · explained in 2 episodes
• Robustness — 9× in this paper · explained in 5 episodes
• Fine-Grained Action Substitution — 7× in this paper
• Linear Probing — 7× in this paper · explained in 2 episodes
• Temporal Binding Success Rate — 7× in this paper
• Benchmarking — 6× in this paper · explained in 3 episodes
• Object-Attribute Modification — 6× in this paper
• State Reversal — 6× in this paper
• Temporal Reasoning — 6× in this paper · explained in 2 episodes
• Temporal Reordering — 6× in this paper
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Distribution Shift — 5× in this paper · explained in 5 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Cumulative Language Prior — 4× in this paper
• Fidelity — 4× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Core — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Hallucination Detection — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Activation Patching — 1× in this paper · explained in 2 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Analyzer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Corruption Robustness — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination Mitigation — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 1× in this paper
• Procedural Knowledge — 1× in this paper · explained in 2 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Lost in Time: A New Temporal Benchmark for VideoLLMs
• SycEval: Evaluating LLM Sycophancy
• FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
• Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration
• Qwen2.5-VL Technical Report
• TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
• VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
• A Survey on LLM-as-a-Judge
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
• Measuring Sycophancy of Language Models in Multi-turn Dialogues
• Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs
• MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
• VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
• Towards Understanding Camera Motions in Any Video
• TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
• VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models
• Exploring Ordinal Bias in Action Recognition for Instructional Videos
• RTime-QA: A Benchmark for Atomic Temporal Event Understanding in Large Multi-modal Models
• VLind-Bench: Measuring Language Priors in Large Vision-Language Models

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