An international research team released the largest video reasoning dataset to date—roughly 1,000 times larger than previous alternatives. Testing reveals that state-of-the-art models including Sora 2 and Veo 3.1 substantially underperform humans on reasoning tasks, suggesting the limitation isn't data scarcity but architectural constraints.
Researchers propose FLoC, a training-free visual token compression framework that selects representative subsets of video tokens using facility location algorithms and lazy greedy optimization. The method works across any video-based large multimodal model without requiring retraining, achieving near-optimal compression ratios on benchmarks including Video-MME, MLVU, LongVideoBench, and EgoSchema.
Researchers have introduced VideoTemp-o3, a unified framework that addresses limitations in long-video understanding by combining temporal grounding and question-answering in a single agentic system. The approach uses a unified masking mechanism during training and reinforcement learning with dedicated reward signals to improve video segment localization and reduce hallucinations.