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arXiv Machine Learning By Pardis Taghavi, Tingyu Guo, Jonas Lossner, Gaurav Pandey, Reza Langari

ActionSplice: In-Flight Action Editing for Interactive World Models

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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