观点分享：VLA解决的是概念认知，无法有效的建模真实世界的四维时空？

Core Viewpoint - The article discusses the importance of world models in intelligent driving, emphasizing that true understanding of the environment requires a high-bandwidth cognitive system rather than merely extending language models [2][3][5]. Summary by Sections World Model vs. Language Model - The world model focuses on spatiotemporal cognition, while the language model addresses conceptual cognition. Language models have low bandwidth and sparsity, making them ineffective for modeling the real world's four-dimensional space-time [2][3]. - The world model aims to establish capabilities directly at the video level, rather than converting information into language first [3][4]. VLA and WA - VLA (Vision-Language Architecture) is essentially an extension of language models, adding new modalities but still rooted in language. In contrast, the world model seeks to create a comprehensive cognitive system [3][5]. - The ultimate goal of autonomous driving is to achieve open-set interactions, allowing users to express commands freely without being limited to a fixed set of instructions [3][4]. Importance of Language - Language remains crucial for three main reasons: 1. Incorporating physical laws such as gravity and inertia into the model [6]. 2. Understanding and predicting object movements in three-dimensional space over time [6]. 3. Absorbing vast amounts of data from the internet, which aids in training autonomous driving systems [7]. Integration of Models - The combination of language models (conceptual cognition) and world models (spatiotemporal cognition) is essential for advancing towards Artificial General Intelligence (AGI) [8]. Industry Trends - The autonomous driving industry is experiencing intense competition, with many professionals considering transitioning to embodied AI due to the saturation of current technologies [9]. - The ongoing debate between VLA and WA represents a larger industry transformation, highlighting the need for innovative solutions to break through current limitations [9]. Community and Resources - A community platform has been established to facilitate knowledge sharing and collaboration among professionals in the autonomous driving field, featuring resources such as learning routes, technical discussions, and job opportunities [25][26].