We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, environmental sound, music and speech. We organize interaction around camera intent: in first-person scenes, it specifies observer motion, while in third-person scenes,...
Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment across long interaction sequences. Recent work on on-policy self-distillation (OPSD) offers complementary supervision by evaluating a policy's...
Zeng-Huang Fu, Zhao-Yang Li, Qiu-Yuan Ai et al.· 0 citations
Tree-structured reinforcement learning trains search agents by comparing alternative continuations and propagating terminal rewards to intermediate decisions. Adaptive expansion, however, creates a statistical asymmetry: an incumbent is selected using its own generation statistic, whereas fresh siblings are sampled aft...
Zeng-Huang Fu, Ning Chen, Ming-Da Jia et al.· 0 citations
The results show that evolving skill memory is not merely an inference-time plug-in: it changes policy learning and the future training distribution while retaining value as optional external memory.
Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai et al.· arXiv.org· 1 citation
VideoCoCo, an agentic dual-engine framework in which executable Blender code serves as a process-level chain of thought, demonstrates that executable code provides an effective, controllable, and inspectable intermediate representation for physically consistent video generation.
Haodong Li, Tianfei Ren, Xiaoxiao Ma et al.· arXiv.org· 8 citations
CoEvoKG is introduced, a framework that turns a knowledge graph into both a source of verifiable training tasks and a persistent evidence memory for agent evolution, closing the loop between model self evolution and knowledge accumulation.
Zhaoyang Li, Zenghuang Fu, Qiuyuan Ai et al.· 0 citations
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