让AI记住你走过的路，还能实时生成世界
现在的AI生成世界，要么只盯着眼前几帧、你一动它就忘了刚才看过什么，要么想记住全部、结果卡到没法实时跑。ReWorld把这两件事拆开：大部分注意力只看最近，少数几个“全局头”负责翻旧账，再用一个按位置索引的地标库，让模型在固定内存下也能找回你几分钟前站过的地方。它把游戏、渲染、真实视频统一到同一套物理尺度上，按同一个键，在所有场景里移动距离都一样。结果是在一分钟的往返路线里，它走回起点时还能还原出最初的画面，而传统滑动窗口早就把证据丢光了。这不是你明天能用的产品，但它指向一个方向：AI世界不再是“一次性布景”，而是能陪你走回头路的那种。
📄 原文摘要(英文)
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 (11.95^circ rotation error and the best camera-motion consistency) and the best generation quality; and on minute-long out-and-back rollouts (64\,s, 384 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.