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This work introduces Anchored Planning, which retrieves a recorded segment whose start and end resemble the current and goal observations, then aims at an observation shortly after its start, and without additional training, planning toward observed targets outperforms the LeWM planner on every task in the authors' lon...
IMPACT is introduced, a scalable Interaction-aware Model training framework with Prior-guided Attention Calibration and Targeting, which consistently outperforms the corresponding MSE-trained baselines, improving interaction fidelity, physical plausibility, and visual quality.
ST-EVO is proposed, which supports dialogue-wise communication scheduling with a compact yet powerful flow-matching based Scheduler to make precise Spatio-Temporal scheduling, and can also perceive the uncertainty of MAS, and possesses self-feedback ability to learn from accumulated experience.
Causal Action Effect Reweighting (CAER), a general training paradigm that redistributes supervision toward the tokens whose predicted future is causally affected by the action, is introduced.
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