Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision models. As these capabilities expand, a central question for the computer-vision community is how far this reach extends, and what remains hard. We evaluate GPT-6 Astra...
H. Rasheed, Mohammed Irfan Kurpath, Bin Ren et al.· 1 citation
Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands,...
Harold Haodong Chen, Rong-Jin Guo, Di-Sen Lan et al.· 0 citations
Streaming video understanding requires models to continuously retain useful visual evidence before future questions are known. Existing approaches primarily manage the growing visual context according to token importance, temporal redundancy, or segment-level relevance, but rarely organize evidence around objects that...
Mingkang Dong, Muxin Pu, Jie Li et al.· arXiv.org· 1 citation
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