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arXiv Machine Learning

ActionSplice: In-Flight Action Editing for Interactive World Models

ActionSplice is an inference framework for chunk‑autoregressive video world models that allows in‑flight action editing without re‑sampling completed evaluations. It formulates the problem as Counterfactual State Transport (CST), using a lightweight corrector to move the backbone representation toward the state induced by a revised action at the same solver step. Two variants, CST*R and CST*T, update either the entire active chunk or only its suffix, achieving significant reductions in rollback‑relative LPIPS and providing speedups over waiting.

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ForeTime-VLA: Causal Future-Token Distillation from a World Action Model for Conveyor-Belt Manipulation

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