Cross-domain predictive framework unifies world modeling across seven distinct systems
Researchers propose JEPA-Anything, a unified framework for building predictive models that generalize across fundamentally different domains, from vision to weather to molecular dynamics. The approach uses orthogonal predictive factorization to decompose prediction targets into independent factors learned through separate pathways, then recombines them in a shared architecture. This addresses a core limitation in world modeling: most systems remain locked to single domains. Success across seven distinct evaluation domains suggests the technique could enable transfer learning for prediction tasks, potentially reducing the need to train specialized models from scratch for each new application.
Modelwire context
ExplainerThe paper doesn't claim to solve world modeling itself, but rather identifies a specific architectural bottleneck: existing predictive systems learn domain-specific representations that don't compose. The factorization approach is the mechanism, not the breakthrough.
This connects directly to 'Embedding Models Measure in Peculiar Ways' from mid-September. That work showed embeddings fail to reliably encode grounded quantities like mass and distance, instead relying on surface patterns. JEPA-Anything tackles the inverse problem: how to build predictive architectures that handle fundamentally different measurement spaces (pixel coordinates, temperature fields, molecular positions) within one framework. Both papers expose the same underlying tension: current neural systems excel at semantic abstraction but struggle when prediction requires fidelity across incommensurable domains. Where embeddings need architectural changes to handle measurement, JEPA-Anything proposes one solution through factorized pathways that respect domain structure.
If the same orthogonal factorization approach maintains performance when tested on a held-out eighth domain (not in the original seven) within the next six months, that confirms the method generalizes beyond the evaluation set. If performance degrades significantly on that new domain, the framework may be overfit to the specific domains chosen for benchmarking.
Coverage we drew on
- Embedding Models Measure in Peculiar Ways · arXiv cs.CL
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MentionsJEPA-Anything · Joint-Embedding Predictive Architecture · Orthogonal Predictive Factorization
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “JEPA-Anything: Learning Predictive Models across Different Worlds”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.