AR-WAM is presented, a visual-conditioned, agent-ready world action model that replaces language with two complementary conditions: a visual grounding prompt and a learnable operation token dictating the atomic skill to execute, predicting scene evolution within compact latent states while decoding actions.
Yi-Cheng Jiang, Ze-Sen Gan, Xiao-Bo Wang et al.· 0 citations
We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each model inherits knowledge, instruction following, and reasoning from one side, continuous-signal percepti...
Xin-Yue Wang, Jia-Cheng Pang, Kun Zhou et al.· 0 citations
Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce \textbf{RSIAgent}, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curriculum, actor,...
Sibo Zhu, Shi-Cheng Fan, Xin-Yue Wang et al.· 3 citations
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