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
Unified multimodal large language models (MLLMs) and multi-agent systems have advanced visual generation. However, three limitations remain. (1) Existing methods often distill task-specific experience with limited generalizability. (2) Reflection is often deferred until task completion. (3) Knowledge is often acquired...
Xun Xu, Jin-Xiu Liu, Zhang-Bo Qiao et al.· 1 citation
High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling. Existing efficient methods mainly distill pretrained models into few-step samplers, a challenging process that depends heavily on teacher-model quality. In this pape...
Jin-Xiu Liu, Xuan Liu, Kang-Fu Mei et al.· 0 citations
ContextWeave is introduced, a longitudinal benchmark that evaluates whether recalled experience improves downstream agent performance in realistic office-work streams and motivates memory systems that optimize not only retrieval relevance but also reliable use during execution.
Bo Wang, Yu Yao, Enxi Wang et al.· 0 citations
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