arXiv Notify me• Jun 30, 2026paper

AdaJEPA: An Adaptive Latent World Model

Yann LeCun(guest)

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Executive Summary

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Test-Time Adaptation in AI Models Latent World Models for Planning

5 quotes

Concerns Raised

Static latent world models are brittle and fail when faced with test-time distribution shifts.
Inaccurate model predictions can lead to catastrophic planning failures for AI agents.

Opportunities Identified

Substantially improving AI planning success in dynamic and unpredictable environments.
Enabling more robust and autonomous agents through continuous, self-supervised model recalibration.
Achieving significant performance gains with computationally cheap, minimal updates (e.g., one gradient step).

Key Themes

Research Findings5

AdaJEPA is an adaptive latent world model that performs test-time adaptation within the closed loop of model predictive control (MPC).

The closed-loop update mechanism in AdaJEPA continuously recalibrates the world model without requiring additional expert demonstrations.

AdaJEPA substantially improves planning success on a range of goal-reaching tasks with as few as one gradient step per model predictive control (MPC) replanning step.

Latent world models that are kept frozen at test time can fail at planning when their predictions become inaccurate, especially under test-time distribution shift.

After training, AdaJEPA uses the observed next-state transition as a self-supervised adaptation signal to update its model before replanning.

Topics

Artificial Intelligence Machine Learning Latent World Models Test-Time Adaptation Model Predictive Control (MPC)Self-Supervised Learning Reinforcement Learning AI Planning Robotics Distribution Shift AI Agents Goal-Reaching Tasks Latent Space Prediction Yann LeCun AdaJEPA

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

AdaJEPA: An Adaptive Latent World Model

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

Test-Time Adaptation in AI Models

Latent World Models for Planning

Self-Supervised Recalibration

Closed-Loop Model Predictive Control (MPC)

Research Findings5

Topics