arXiv:2606.09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM). These methods are, however, too computationally expensive and ineffective for long-horizon planning. Furthermore, these methods typically require an explicit image of the goal state, which is not always possible in real-world tasks. In this work, we tackle these limitations by proposing Forward-Forward-JEPA (FF-JEPA), a hierarchical
The continuous drive for more autonomous and capable AI systems motivates research into overcoming current limitations in planning and world modeling.
Improved long-horizon planning in world models reduces computational costs and dependence on explicit goal states, which is critical for practical AI applications.
FF-JEPA's hierarchical approach could significantly improve the efficiency and applicability of AI planning, accelerating the development of more advanced agents.
- · AI research labs
- · Robotics companies
- · Autonomous system developers
- · Companies reliant on computationally intensive planning methods
- · AI models restricted by short-horizon planning
More efficient and effective long-horizon planning for AI agents.
Accelerated development of more complex and general-purpose autonomous AI systems.
Increased adoption of AI agents in real-world scenarios across various industries.
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Read at arXiv cs.AI