Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for plann...
Yu-Han Guo, Jin-Ming Liu, Liang Xu et al.· 0 citations
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for plann...
Yu-Han Guo, Jin-Ming Liu, Liang Xu et al.· 0 citations
To serve as real-world personal assistants, streaming video models need persistent memory that retains past experiences for later use. Yet existing streaming benchmarks and methods often focus on individual continuous videos or short clips, overlooking that real-world interactions are often intermittent and require mem...
Jian-Guo Huang, Jin-Ming Liu, Qi-Yao Wang et al.· 0 citations
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