World models are widely explored in embodied intelligence, yet they typically predict distinct evolutions of the world and the ego within a single stream, where the world captures persistent instruction-agnostic scene regularities and the ego captures robot-centric instruction-conditioned dynamics. This world-ego entanglement leads to a degradation in long-horizon embodied scenarios, particularly in hybrid tasks with interleaved navigation and manipulation behaviors. In this paper, we introduce \emph{World-Ego Modeling}, a new conceptual paradigm that decomposes future evolution into world and ego components. We define the world-ego boundary from three perspectives, i.e., motion-, semantic-, and intention-based views, and analyze three disentanglement strategies with post-, pre-, and full disentanglement. Further, we instantiate this paradigm as the World-Ego Model (WEM), a unified embodied world model that couples an implicit separate world-ego planner with a cascade-parallel mixture-of-experts (CP-MoE) diffusion generator. To enable rigorous evaluation, we further construct HTEWorld, the first benchmark for long-horizon world modeling with hybrid navigation-manipulation tasks, providing 125K video clips (over 4.5M frames) with fine-grained action annotations and 300 multi-turn evaluation trajectories (over 2K instructions). Extensive experiments show that WEM achieves state-of-the-art performance on HTEWorld while remaining competitive on existing manipulation-only benchmarks.

翻译：世界模型在具身智能领域被广泛探索，但现有方法通常在同一数据流中预测世界与自我的不同演化——其中世界捕捉与指令无关的持久场景规律，自我则捕捉以机器人为中心、受指令驱动的动态信息。这种世界-自我纠缠会导致长周期具身场景性能退化，尤其在交织导航与操控行为的混合任务中。本文提出世界-自我建模这一新概念范式，将未来演化分解为世界分量与自我分量。我们从运动、语义和意图三个视角定义世界-自我的边界，并分析后解耦、前解耦与全解耦三种策略。进一步将该范式实例化为世界-自我模型(WEM)——一种统一具身世界模型，通过隐式分离的世界-自我规划器与级联并行专家混合(CP-MoE)扩散生成器实现耦合。为进行严格评估，我们构建了首个面向导航-操控混合任务的长周期世界建模基准HTEWorld，包含12.5万个视频片段（超过450万帧）及其细粒度动作标注，以及300条多轮评估轨迹（包含2000余条指令）。大量实验表明，WEM在HTEWorld上达到最优性能，同时在现有纯操控基准上保持竞争力。