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arXiv AI By Divyansh Jha, Yuanfang Xie, Brennen Yu, Varan Mehra

BRo-JEPA: Learning Modular Transformations in Latent Space

Read the original on arXiv AI →

The paper introduces BRo-JEPA, a world model that learns modular arithmetic operations as rotations in latent space. Using MNIST and EMNIST datasets, BRo-JEPA achieves near-perfect zero‑shot generalization to unseen operations, outperforming standard supervised and JEPA baselines by a large margin. The model demonstrates that latent transformations can encode underlying algebraic structures, enabling strict zero‑shot operation generalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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