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This perspective outlines a critical research agenda for Joint-Embedding Predictive Architectures (JEPAs), arguing that interpretability must evolve from simple representation decoding to understanding causal interactions within latent world models. The proposed four-stage framework—Ground, Structure, Verify, and Act—aims to bridge the gap between internal predictive states and human-understandable mechanisms for planning and control. By shifting focus toward validating how latent variables drive action, this work addresses a fundamental bottleneck in scaling autonomous AI systems.
World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
arXiv:2610.25296v1 Announce Type: new Abstract: Emerging Joint-Embedding Predictive Architectures (JEPAs) are moving beyond representation learning towards latent world models that support prediction, planning, and control. Yet their latent worlds naturally lack a human-interpretable interface. In this Perspective, we argue that as JEPA latents become internal states for prediction and action, interpretability must move beyond what they encode to how latent variables interact, evolve, and causally drive prediction. To this end, we envision a four-stage research agenda, involving Ground, Structure, Verify, and Act, to guide this shift toward actionable latent-world understanding. Within our framework, existing JEPA interpretability efforts provide early evidence for Ground, while systematic analysis of latent structure, causal verification, and intervention remains largely unexplored. Overall, this Perspective aims to call for a broader latent-world interpretability agenda that shifts attention from decoding representations toward understanding, validating, and acting on latent worlds.
Abstract:Emerging Joint-Embedding Predictive Architectures (JEPAs) are moving beyond representation learning towards latent world models that support prediction, planning, and control. Yet their latent worlds naturally lack a human-interpretable interface. In this Perspective, we argue that as JEPA latents become internal states for prediction and action, interpretability must move beyond what they encode to how latent variables interact, evolve, and causally drive prediction. To this end, we envision a four-stage research agenda, involving Ground, Structure, Verify, and Act, to guide this shift toward actionable latent-world understanding. Within our framework, existing JEPA interpretability efforts provide early evidence for Ground, while systematic analysis of latent structure, causal verification, and intervention remains largely unexplored. Overall, this Perspective aims to call for a broader latent-world interpretability agenda that shifts attention from decoding representations toward understanding, validating, and acting on latent worlds.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.25296 [cs.LG]
(or arXiv:2610.25296v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.25296 Focus to learn more arXiv-issued DOI via DataCite
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