arXiv Computer Vision
ProAR: Learning Prospective Reasoning with Autoregressive Video Models
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arXiv AI
Aug 18
Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
arXiv:2608. 15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments.
arXiv Computer Vision
5d ago
VR-JEPA: Learning Contrastive-State Latent Guidance for Generation-based Video Reasoning
arXiv:2609.40129v1 Announce Type: new Abstract: Reasoning through video generation offers a promising path toward visual intelligence by modeling latent visual states and their dynamics. However, cur...
arXiv AI
Jul 10
OpenCoF: Learning to Reason Through Video Generation
arXiv:2607. 08763v1 Announce Type: cross Abstract: Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences.
arXiv AI
Aug 25
VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting
VisionCoach is an input‑adaptive reinforcement learning framework that enhances spatio‑temporal grounding in video reasoning by using visual prompting during training. The system selectively applies visual prompts to challenging inputs, amplifying question‑relevant evidence and suppressing distractors, and then internalizes these improvements through self‑distillation so that inference can be performed on raw videos without prompts. Experiments on multiple benchmarks (V‑STAR, VideoMME, World‑Sense, VideoMMMU, PerceptionTest, and Charades‑STA) show that VisionCoach achieves state‑of‑the‑art performance while maintaining a single efficient inference pathway.
arXiv AI
Jun 2
Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion
arXiv:2606. 00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos.
arXiv AI
Aug 18
Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning
arXiv:2608. 16316v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) for video reasoning have long been hindered by the high computational cost of processing vast amounts of visual information.