What's the Catch? Evaluating Temporal Consistency in Vision-Language Models

arXiv:2608.23474 · cs.CL, cs.AI, cs.CV · Submitted 2026-08-24 · Read on arXiv

cs.CL, cs.AI, cs.CV

Submitted: 2026-08-24

Updated: 2026-09-12

Terminology

• Benchmark — 39× in this paper · explained in 2 episodes
• Accuracy — 34× in this paper · explained in 1 episode
• Temporal Reasoning — 19× in this paper · explained in 2 episodes
• Consistency — 17× in this paper · explained in 3 episodes
• Temporal consistency — 15× in this paper · explained in 2 episodes
• Grounding — 10× in this paper · explained in 3 episodes
• Core — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 3 episodes

Sources

• Lost in Time: A New Temporal Benchmark for VideoLLMs
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs
• Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
• SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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