arXiv Computer Vision By 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)

Platonic Representation Hypothesis on World Models

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arXiv AI
Sep 1

Social-JEPA: Emergent Geometric Isomorphism

arXiv:2603.02263v3 Announce Type: replace-cross Abstract: World models compress rich sensory streams into compact latent codes that anticipate future observations. We let separate agents acquire such...

By Haoran Zhang, Youjin Wang, Yi Duan, Rong Fu, Dianyu Zhao, Sicheng Fan, Shuaishuai Cao, Wentao Guo, Xiao Zhou
arXiv Computer Vision
Sep 22

GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation

The paper introduces the Geometry‑Native Autoencoder (GAE), a compact latent space that can be decoded into appearance, depth, camera parameters, and point maps, enabling 3D‑consistent world generation. By reparameterizing a geometry foundation model’s features, GAE replaces traditional appearance‑centric latents and improves visual quality and 3D coherence, achieving significant reductions in FVD and camera‑trajectory error on benchmark datasets. The work demonstrates that a geometry‑native latent space can serve as a shared interface between perception and generation models.

By Jiahao Lu, Minghao Yin, Wenbo Hu, Hengyu Liu, Wang Zhao, Sai-Kit Yeung, Ying Shan, Yuan Liu
arXiv Machine Learning
Jun 5

LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

arXiv:2603. 19312v3 Announce Type: replace Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse.

By Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, Randall Balestriero
arXiv Machine Learning
Sep 24

What Converges in the Platonic Representation Hypothesis? Structure over Geometry

The paper investigates the Platonic Representation Hypothesis, which posits that more capable models converge toward shared representations. By distinguishing relational structure (which samples are related) from metric geometry (quantitative relations like distances), the authors develop a controlled $2 imes2$ framework to evaluate both aspects at local and global scales. Their findings show that relational structure consistently converges across vision‑language and video‑text models, while metric geometry converges much more weakly, a pattern that persists even when using a Riemannian metric approximation.

By Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung