arXiv:2609.38413v1 Announce Type: new
Abstract: Vision-language models (VLMs) can answer questions about hour-long videos, but processing every frame is prohibitively expensive, even though the evide...
By Susan Liang, Jianmin Wu, Daxiang Dong
arXiv:2609.37950v1 Announce Type: new
Abstract: Video understanding agents acquire evidence through an executable harness that controls what they observe and how they use those observations. However,...
By Bingjun Luo, Jialin Guo, Siqi Li
arXiv:2609.12818v1 Announce Type: new
Abstract: Long video understanding often behaves like a visual needle-in-a-haystack problem: query-relevant evidence is sparsely distributed across long temporal...
By Sen Yang, Boqiang Duan, Jing Yang, Weihao Bo, Jie Liu, Boyuan Tong, Ze Feng, Wenkang Zhang, Jingdong Wang, Hua Wu
arXiv:2606.08091v2 Announce Type: replace
Abstract: Agentic long video generation requires planning, tool orchestration, and cross-clip coordination over a long horizon. Most existing video agents ei...
By Jianhui Wei, Yan Zhang, Jie Tan, Hengchuan Zhu, Xiaotian Zhang, Ziyi Chen, Daoan Zhang, Wei Xu, Yeying Jin, Zuozhu Liu
arXiv:2608. 12290v1 Announce Type: cross Abstract: Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows.
By Aman Tyagi, Hemanth Boinpally, Jonathan Chen, Douglas Gebert, Steven Hickson
Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes.