世界行動模型：綜述

摘要

世界行動模型（World Action Models, WAMs）是一類具身預測行動模型，能為行動提供對未來的預測。近期WAMs重新利用大型視頻生成模型，而另一條平行研究方向則依賴於語言或視覺語言骨幹網絡，不具備視頻生成核心。這種快速擴展模糊了廣泛世界模型、視頻生成模型、基於行動的視頻世界模型、視覺-語言-行動策略以及WAMs之間的界線。本綜述為該領域提供了統一的架構。首先釐清這些界線，接著透過兩種互補視角組織現有研究。第一種視角關注每種方法需要生成的內容，涵蓋渲染的未來、潛在的未來以及無視頻生成的行動推理。第二種視角則從預測基礎、骨幹網絡、行動耦合與部署模式四個維度拆解每種方法。這種剖析支持對交互性、因果性、持久性、物理合理性與泛化能力的統一討論，隨後探討數據、評估與開放性挑戰。在這些軸線上，一個一致的設計模式浮現：WAMs並非簡單帶有行動頭的視頻生成器，而是預測行動方法，其設計選擇在表徵豐富度與計算、記憶體、延遲及行動標籤成本之間進行權衡。該領域正朝生成更少未來內容、同時保留控制所需資訊的方向發展。本綜述的首頁可於 https://world-action-models.github.io/ 瀏覽。

English

World Action Models (WAMs) are embodied predictive-action models that make a forecast of the future available to action. Recent WAMs repurpose large video generation models, and a parallel line relies on language or vision-language backbones without a video-generation core. This rapid expansion has blurred the boundary among broad world models, video generation models, action-grounded video world models, Vision-Language-Action policies, and WAMs. This survey gives the field a common account. It first clarifies these boundaries, then organizes existing works through two complementary views. The first view asks what each method is required to generate, spanning rendered futures, latent futures, and video-generation-free action reasoning. The second view decomposes each method by predictive substrate, backbone, action coupling, and deployment regime. This anatomy supports a unified discussion of interactability, causality, persistence, physical plausibility, and generalization, followed by data, evaluation, and open challenges. Across these axes, a consistent design pattern emerges: WAMs are not simply video generators with action heads, but predictive-action methods whose design choices trade representational richness against compute, memory, latency, and action-label cost. The field is moving toward methods that generate less of the future while preserving what control requires. The survey homepage is available at https://world-action-models.github.io/.