实现 Agent 能力的泛化 ，是否一定需要对世界表征？

Group 1 - The article discusses the necessity of world representation for achieving generalized agent capabilities, highlighting the ongoing debate between model-free and model-based paradigms in AI [4][5][8] - It emphasizes that modern AI agents are expected to perform complex tasks autonomously, distinguishing them from simple bots through their ability to generalize [5] - The model-free paradigm suggests that intelligent behavior can emerge from direct perception-action loops without explicit internal representations, while the model-based paradigm argues for the need of a rich internal predictive representation of the world [6][7] Group 2 - The article references recent research by DeepMind that formalizes the debate between model-free and model-based approaches, demonstrating that agents with generalization capabilities inherently internalize world representations [6][7] - It outlines a core theorem indicating that any generalized agent must have a high-quality world model to achieve long-term capabilities, contradicting the notion that one can bypass representation [7] - The discussion shifts from whether representation is needed to how it should be constructed, noting that existing world model paradigms are not without flaws and there is a lack of consensus in the field [8]