JEPA · 2026-10-20
World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
Jize Wang, Yongcheng Jing, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors argue that JEPA latent spaces should be interpreted as latent world models, not just decoded representations. They propose a four-stage agenda—Ground, Structure, Verify, Act—to move from (1) mapping latents to world variables, to (2) recovering relations/dynamics, to (3) causally verifying predictive mechanisms via interventions, and (4) using verified understanding to diagnose and improve model behavior.
Why it matters
Because JEPA prediction happens in an abstract latent space, interpretability should explain how latent variables interact, evolve, and causally drive prediction and action. The paper distinguishes representational accessibility (what can be decoded) from world-model understanding (how information is organized and causally used), and provides a blueprint for turning interpretability into actionable diagnosis and intervention.
Method
- Define JEPA latent world interpretability as interpreting the predictive system that transforms latent states over time (and possibly depends on actions).
- Use a four-stage blueprint: Ground (latents ↔ external variables), Structure (relations/dynamics among grounded factors), Verify (causal role via controlled interventions), Act (use verified mechanisms for targeted intervention and diagnosis).
- Distinguish decodability from understanding: decoding a variable does not show how the predictor uses it or whether it relies causally on it.
Limitation
The authors note an important challenge for Verify: interventions must change the target factor while preserving other relevant information and keeping the resulting latent state plausible; developing and validating such interventions is an important direction for future research.
Abstract (from arXiv)
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.
Related papers
- Inside-JEPA: From Prediction to Relational Grounding in Latent World Models
- Do-JEPA: From Masking to Intervention in Latent World Models
- Learning Intrinsic Dynamics in Latent World Models
- Exploring Temporal Context-Aware Physical Grounding in Latent World Models
- One-Step Prediction Fidelity Is a Poor Proxy for Planning Performance in JEPA World Models
Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-18 · Indexed by Pub-AI: 2026-10-20 UTC
World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
Preprint posted: 2026-10-18 · Original record ↗
- arxiv
- 2610.25296
Explicit source versions (1)
- v1 · 2026-10-18
- Preprint available
Source date: 2026-10-18 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
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.