World Model Interpretability: From Decoding Representations to Understanding Latent Worlds

Jize Wang · hf · 2026-10-20

A Perspective paper by Jize Wang, Yongcheng Jing, Dacheng Tao et al. argues for a new direction in JEPA world-model interpretability.

• JEPA models are moving beyond representation learning into latent world models supporting prediction, planning, and control, with applications spanning robotics, 3D scene understanding (V-JEPA 2), medical analysis, and scientific discovery—yet their latent worlds lack human-interpretable interfaces.
• Core claim: interpretability must shift from decoding what representations encode to analyzing how latent variables interact, evolve, and causally drive prediction.
• The authors propose a four-stage research agenda—Ground, Structure, Verify, Act—to guide actionable latent-world understanding, positioning existing JEPA interpretability efforts as early evidence.

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