AI预测未来：不是全都要，而是只取所需
AI预测未来，不是把整个未来都画出来，而是只生成决策需要的那一小块。这篇综述梳理了「世界动作模型」——一种能预测未来状态并据此行动的AI系统。它发现，这类模型正从「生成完整视频」转向「只生成关键信息」，因为算力、延迟和标注成本都有限。比如，一个机器人要抓杯子，它不需要预测整个房间未来10秒的每一帧，只需要知道杯子会怎么动。这种「少即是多」的设计，让AI更高效、更实用。它不是你明天就能用的工具，但揭示了AI决策系统的一个核心趋势：未来预测，精准比完整更重要。
📄 原文摘要(英文)
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/.