arXiv AI
Scaling Video Generation for Reasoning: At What Cost?
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arXiv AI
Jun 2
Video Reasoning without Training
arXiv:2510. 17045v2 Announce Type: replace-cross Abstract: Video reasoning using Large Multimodal Models (LMMs) relies on costly reinforcement learning (RL) and verbose chain-of-thought, resulting in substantial computational overhead during both training and inference.
arXiv AI
Jul 1
Are Video Reasoning Models Ready to Go Outside?
arXiv:2603. 10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion.
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
Oct 1
SYNCR: A Cross-Video Reasoning Benchmark with Synthetic Grounding
SYNCR is a synthetic benchmark designed to evaluate multimodal large language models on cross‑video reasoning. It contains 4,000 question‑answer pairs across 4,827 unique videos, covering tasks in temporal alignment, spatial tracking, comparative reasoning, and holistic synthesis. The benchmark reveals a significant performance gap between current models and humans, with models excelling at temporal ordering but struggling with precise physical and spatial reasoning.
arXiv Machine Learning
Aug 27
VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
VBVR-Pro is a closed‑loop testbed that enables native visual reasoning through generation, offering 300 procedurally generated tasks that scale training and allow strong transfer to external benchmarks. It supplies verifiable reward scorers based on deterministic, task‑specific rules, outperforming VLM‑as‑a‑judge approaches and providing reliable signals for reinforcement learning. The suite also facilitates controlled modality studies, revealing that video generation excels at persistent spatiotemporal tracking while interleaved generation offers a compute‑efficient alternative, and highlights the importance of vision‑native trajectories for reasoning.
arXiv Computer Vision
Oct 5
ProAR: Learning Prospective Reasoning with Autoregressive Video Models
ProAR introduces a goal‑oriented reasoning framework for autoregressive video models, adding goal‑frame prediction via an asymmetric attention mask and future representation self‑alignment to guide intermediate states. These mechanisms combine sparse target supervision with dense step‑wise guidance, enabling coherent, goal‑directed generation while keeping computational costs modest. Experiments demonstrate consistent performance gains across visual reasoning benchmarks and training efficiency, achieving comparable results to fully trained baselines with only 25% of the training steps.