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A unified latent-space framework for image and video diffusion models that achieves the sota performance among various metrics and further improves optimization stability and achieves the highest VBench quality, semantic, and total scores among the evaluated methods.
LLMODE is proposed, a token-efficient framework for irregular spatio-temporal forecasting with a frozen LLM backbone that shows competitive overall performance, with clearer advantages under sparse or dynamically complex irregular sampling.
This work proposes a co-evolution roadmap for physical intelligence centered on theembodied brain, a long-term model target for integrating multimodal context, comparing candidate interventions, and issuing state-transition or capability requests rather than direct actuator commands.
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