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Research PaperResearchia:202608.25002

ReWorld: An Interactive World Model with Long-Horizon Memory

Zhifei Chen

Abstract

An interactive world model must follow the user's actions, remember the places it has shown, and stream in real time. The tension is structural: control wants a short horizon, memory wants an unbounded one. ReWorld separates the two during training and bounds them at inference. Mixed per-head attention windows confine most heads to the recent past while a small set of global heads attends over the entire history, and random head routing keeps either capability from binding to particular heads; r...

Submitted: August 25, 2026Subjects: AI; Artificial Intelligence

Description / Details

An interactive world model must follow the user's actions, remember the places it has shown, and stream in real time. The tension is structural: control wants a short horizon, memory wants an unbounded one. ReWorld separates the two during training and bounds them at inference. Mixed per-head attention windows confine most heads to the recent past while a small set of global heads attends over the entire history, and random head routing keeps either capability from binding to particular heads; random chunk dropping makes sparse histories in-distribution. At inference the whole past lives under a fixed budget: a bounded KV cache backed by a pose-indexed landmark bank, from which the model retrieves the landmarks nearest the current pose. A metric-scale-aligned data engine places eight sources -- Unreal-rendered fly-throughs, game roaming, and real-world footage -- on one physical action scale, so the same key press moves the camera the same distance in every source, and palindrome trajectories supply the revisit evidence that memory training needs. Distribution-matching distillation confined to a LoRA adapter then compresses sampling to four steps: one backbone serves both a high-fidelity multi-step mode and a real-time interactive one, streaming 704x1280 video across photorealistic, game-style, and stylized worlds. Under a three-axis protocol covering action following, long-horizon recall, and video quality, against six recent interactive world models it attains the best control fidelity (11.95โˆ˜11.95^\circ rotation error and the best camera-motion consistency) and the best generation quality; and on minute-long out-and-back rollouts (6464,s, 384384 latents), its fixed 12-chunk cache still regenerates the starting view -- at rollout lengths where a sliding window has long evicted the evidence and full-KV attention runs out of memory.


Source: arXiv:2608.23565v1 - http://arxiv.org/abs/2608.23565v1 PDF: https://arxiv.org/pdf/2608.23565v1 Original Link: http://arxiv.org/abs/2608.23565v1

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Submission Info
Date:
Aug 25, 2026
Topic:
Artificial Intelligence
Area:
AI
Comments:
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