ReWorld: An Interactive World Model with Long-Horizon Memory
Score
78提出控制/记忆解耦的混合头注意力与位姿索引地标库，结构创新明显，填补实时长时世界模型空白。
实验覆盖三种能力轴、六款基线，并包含 64 秒长回环消融验证，证据链条完整。
四步采样与固定预算缓存让实时交互和长时记忆具备工程落地参考，减少部署显存压力。
预印本针对当前交互世界模型的实时长时记忆痛点，方法路线和实验设置都很新。
来自 arXiv AI 预印本，方法细节丰富，但尚未经过正式同行评审。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The architecture is well-designed and the evaluation protocol spans action following, long-horizon recall, and video quality, delivering state-of-the-art results against six baselines.
AI commentary
「This paper puts the control-memory tension on the table and offers a practical engineering solution. Teams working on interactive video generation or embodied AI should look closely at the pose-indexed landmark bank and four-step sampling; the preprint protocol, however, still deserves broader validation in open-world scenarios.」
Quick overview
ReWorld introduces an interactive world model that separates control from long-horizon memory during training and bounds both at inference. Most attention heads are confined to the recent past while a few global heads attend over the full history; random head routing and random chunk dropping keep sparse histories in distribution. At inference, a fixed-budget KV cache backed by a pose-indexed landmark bank retrieves landmarks nearest the current pose, allowing a 12-chunk cache to regenerate the starting view on 64-second out-and-back rollouts where sliding windows have lost evidence and full-KV attention runs out of memory. A metric-scale-aligned data engine unifies eight sources, and LoRA-based distillation compresses sampling to four steps for real-time 704x1280 streaming. ReWorld achieves the best control fidelity and generation quality against six recent interactive world models.
- Separation of control and memory: most attention heads look at the recent past, a few global heads cover the full history, and random head routing prevents either capability from binding to fixed heads.
- Bounded long-horizon memory: a pose-indexed landmark bank backed by a fixed-budget KV cache regenerates the starting view on 64-second/384-latent out-and-back rollouts.
- Metric-scale-aligned data engine: eight data sources share one physical action scale, and palindrome trajectories supply revisit evidence for memory training.
- Four-step real-time inference: LoRA-based distillation lets one backbone serve both high-fidelity multi-step generation and real-time interactive 704x1280 streaming.
Full text
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