AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Recent advances in video world models have enabled plausible interactive environments, yet long-horizon generation places a fundamental demand on visual memory: the previously observed world must remain consistent as the model continues to generate. Existing autoregressive approaches often implicitly rely on the genera...
Modeling human reactions to spatial audio requires understanding both what is heard and where it comes from. Prior work generates motion offline from complete acoustic and spatial input sequences. Humans, however, react to sounds without knowing what they will hear next. We present Sonic-AMG, to our knowledge the first...
STAM is introduced, a dataset of motion sequences paired with spatial audio and detailed textual annotations whose rich vocabulary affords precise and nuanced specification of human motions, and MoSAT, a latent flow-matching framework for full-body motion generation jointly conditioned on natural-language intent and di...
This work introduces HumanCLAW, an evaluation framework that decouples action decision-making from low-level execution in a vision-language model (VLM), and builds HumanCLAW-Bench, a database of 1,218 long-horizon, egocentric find-navigate-interact episodes across 41 indoor scenes.
This work proposes Ms.Forcing, an efficient streaming video generation paradigm that adapts spatial granularity to each state's noise level and introduces Homogeneous-Noise-Level DMD, which assembles each fake video from clean predictions sharing the same source noise level, thereby reducing the mismatch between DMD tr...
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