ActionSplice: In-Flight Action Editing for Interactive World Models
Abstract
ActionSplice introduces counterfactual state transport to splice revised actions into chunk-autoregressive video world models without replaying completed evaluations, improving fidelity and speed.
Chunk-autoregressive video world models typically condition each generated chunk on one action. An action received during sampling must therefore wait for the next chunk, condition future solver evaluations on a state produced under the previous action, or trigger rollback that repeats completed evaluations. We introduce ActionSplice, an inference framework that formulates this problem as Counterfactual State Transport (CST). A lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action at the same solver step. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. The retargeting variant CST*{R} updates the entire active chunk, while the temporal-splicing variant CST*{T} preserves a temporal prefix and updates only the suffix. Across minWM-Wan Action2V and HY-WM1.5, CST*{R} reduces rollback-relative LPIPS by 61.5% and 75.9% relative to direct condition swapping. CST*{T} reduces suffix LPIPS by 56.1% and 77.5%, respectively, while providing 2.73times and 1.69times pixel-ready speedups over waiting. Under the HY-WorldPlay protocol, CST_{R} obtains a PSNR of 25.66 dB, an SSIM of 0.6902, and an LPIPS of 0.1337 against the original rollout.
Community
ActionSplice enables in-flight action editing for chunk-autoregressive video world models. At interruption step r, a lightweight corrector performs Counterfactual State Transport, mapping the active backbone-native representation toward the same-step state induced by the revised action while keeping the world model and sampler frozen and avoiding replay of completed evaluations. CST-R retargets the entire active chunk; CST-T preserves a temporal prefix and edits only the suffix. Across minWM–Wan Action2V and HY-WM1.5, CST-R reduces rollback-relative LPIPS by 61.5% and 75.9% relative to direct condition swapping. CST-T reduces suffix LPIPS by 56.1% and 77.5%, with 2.73× and 1.69× pixel-ready speedups over waiting.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models (2026)
- DriveCache: Action-Aware Caching for Driving World Model Inference (2026)
- FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution (2026)
- Foresight Without Seeing: Latent Futures for World Action Models (2026)
- AlayaWorld: Interactive Long-Horizon World Modeling - Full Technical Report (2026)
- Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation (2026)
- CheckVLA: Execution-Time Verification with Action-Conditioned World Model for Long-Horizon Mobile Manipulation (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Get this paper in your agent:
hf papers read 2609.08230 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper