arXiv:2605.31111v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn compact latent world models by predicting future embeddings, but no single coordinate of the latent is designated to encode task progression. We carve the JEPA latent into two orthogonal subspaces with disjoint roles: a low-dimensional progression subspace shaped by a cosine-margin triplet loss, and a high-dimensional content subspace regularised by the existing SIGReg objective of LeWM. We prove that the two anti-collapse forces act on disjoint coordinates, so they compose additively rather
This research builds on recent advancements in Joint-Embedding Predictive Architectures (JEPAs) and addresses a known limitation in disentangling latent space representation, moving towards more interpretable and controllable AI world models.
The proposed method could lead to more efficient, robust, and generalizable AI models by separating task progression from content, which is crucial for complex autonomous systems and foundational AI research.
AI world models can now theoretically better understand and represent sequential tasks and content independently within their latent space, offering improved control and interpretability for predictive architectures.
- · AI researchers (fundamental ML)
- · Developers of predictive AI systems
- · Robotics (path planning & control)
- · Autonomous agents
- · Developers using less efficient, 'black box' latent representations
More sophisticated and interpretable latent world models become feasible, enabling better task decomposition.
Improved latent space disentanglement could accelerate the development of more capable and reliable AI agents.
This could contribute to the foundational blocks required for more complex, self-improving AI systems, impacting industries reliant on sophisticated AI.
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Read at arXiv cs.LG