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Platonic Representation Hypothesis on World Models

arXiv:2608.23720v1 Announce Type: new 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.

SourcearXiv Computer VisionAuthor: Wenhow Li (The Hong Kong University of Science and Technology), Chengwei MA (The Hong Kong University of Science and Technology), Hui Xiong (The Hong Kong University of Science and Technology), Ying-Cong Chen (The Hong Kong University of Science and Technology), Lei Zhang (The Hong Kong University of Science and Technology)

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[Submitted on 24 Aug 2026]

Title:Platonic Representation Hypothesis on World Models

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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.

Comments: 18 pages, 10 figures, 2 tables. Wenhow Li and Chengwei MA contributed equally. Project page: this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2608.23720 [cs.CV]

(or arXiv:2608.23720v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.23720

arXiv-issued DOI via DataCite (pending registration)

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From: Li Wenhao [view email] [v1] Mon, 24 Aug 2026 18:08:30 UTC (3,880 KB)

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