Platonic Representation Hypothesis on World Models

2026-08-24

AI Summary

研究通过提出预测一致性假设，利用DINO世界模型实验，探索了不同模型如何趋向共享的潜在结构，解决了世界模型学习表征本质理解不足的问题。

Abstract

World models have demonstrated significant potential for perceiving and simulating complex environments. Despite their strong performance, the fundamental nature of their learned representations remains poorly understood. In this paper, we investigate the Platonic Representation Hypothesis within this domain by proposing the Predictive Consistency Assumption: we posit that the optimization of a shared state transition objective acts as a selective pressure that encourages heterogeneous models to converge toward a shared latent structure. Through systematic experiments with the DINO World Model (DINO-WM), in which we vary visual encoders to create heterogeneous models, we find that capable world models evolve toward geometrically similar internal structures. Moreover, via model stitching, we show that the internal features of one world model can be mapped to another with limited performance degradation, providing evidence of functional compatibility. Our findings suggest that the pursuit of predictive consistency can promote shared, transition-compatible latent structure across world models.

Problem

• World Models
• Platonic Representation Hypothesis
• Predictive Consistency
• Latent Structure
• Shared State Transition

Innovation

• Predictive Consistency Assumption
• Platonic Representation Hypothesis
• World Models
• DINO-WM
• Model Stitching