2026 将近，世界模型到底更「世界」了吗？

Core Viewpoint - The recent launch of GWM Worlds and GWM Robotics by Runway pushes video generation towards an interactive "world simulation" paradigm, reigniting discussions on the definition and scope of "world models" as interfaces for creation and interaction, simulators for training and evaluation, or cognitive frameworks for reasoning and decision-making [1]. Group 1: Evolution of World Models - Over the past two years, world models have evolved to be considered on par with LLMs in the AGI landscape, transitioning from a narrow definition focused on reinforcement learning to a broader understanding that includes generative modeling [4]. - Initially, world models were seen as internal environment models for agents, predicting future states based on current conditions and actions, allowing for internal simulation and decision-making [5]. - The engineering perspective defined world models as a combination of three capabilities: compressing high-dimensional perception into usable representations, predicting future states over time, and utilizing predictions for planning and decision-making [6]. - By 2024, the understanding of world models expanded to encompass general world evolution modeling, with a trend from language generation to image generation, and ultimately to 3D and world generation [6]. - The boundaries of the world model concept have become more ambiguous, with ongoing debates about the nature of representations, the incorporation of physical laws, and the organization of input relationships [6]. Group 2: Industry Layout and Trends - Major companies are investing in world models, questioning whether they are enhancing their "data engines" or building new frameworks for "spatiotemporal cognition" [3]. - In February 2024, OpenAI referred to the video generation model Sora as "world simulators," emphasizing their ability to learn the three-dimensional structure and physical laws of the real world [6]. - Concurrently, LeCun introduced V-JEPA, which focuses on predicting masked video segments in abstract representation space, allowing for higher training efficiency by discarding unpredictable information [6]. - The current discourse has shifted from whether to develop world models to how to model them, with debates on whether to abstract from pixel levels or to directly operate in abstract spaces [7]. - There is a recognition that existing approaches may only capture partial physical laws, indicating a need for representations of isolated objects and a priori laws of change across space and time to achieve a coherent world model [7]. Group 3: Definition and Ambiguity of World Models - By 2025, world models are positioned alongside LLMs, with companies like Google DeepMind, Meta, and Nvidia shifting focus from pure LLMs to world models, aiming for "Physical AI + superintelligence" due to stagnation in LLM advancements [8]. - The distinction between world models and existing generative AI lies in the former's goal to construct internal representations of environments that include physical, temporal, and spatial dimensions for planning and decision-making [9]. - The term "world model" has become ambiguous, referring to latent states within systems, game-like simulators for training agents, or any content pipeline capable of generating navigable 3D scenes [9]. - An analysis from Entropy Town in November 2025 categorized world models into three technical routes: interface, simulator, and cognitive framework, highlighting the ongoing ambiguity in the field [9].