Dual-Channel Grounded World Modeling (DCGWM): Structural Prevention of Objective Interference Collapse via Heterogeneous External Grounding with Inward-Only Gradient Flow
arXiv:2606.18688v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) are a leading approach to world model representation learning. We identify a failure mode in JEPA-based world models grounded against two qualitatively distinct external signals: physical dynamics (sparse, high-magnitude, constraint-satisfying gradient corrections) and social-behavioral dynamics (diffuse, distribution-matching corrections). We term this Objective Interference Collapse (OIC): we argue that joint learning in a shared latent space causes the dominant channel to systematically collap
The increasing complexity of AI systems, particularly world models, is exposing fundamental architectural limitations in how they process and integrate diverse forms of information.
Improving the robustness and learning capabilities of AI world models is critical for advancing general AI and autonomous systems, enabling them to handle real-world complexity more effectively.
This research proposes a new architectural paradigm for world models, suggesting a move away from uniform latent spaces when integrating heterogeneous data, which could lead to more stable and capable AI.
- · AI researchers in world modeling
- · Developers of embodied AI and robotics
- · Companies building advanced AI agents
- · Architectures relying solely on shared latent spaces for heterogeneous data
DCGWM prevents 'Objective Interference Collapse' in JEPA-based world models by segregating gradient flows from distinct data modalities.
More reliable and effective integration of multimodal data will accelerate the development of robust autonomous systems.
This could lead to a new generation of AI agents capable of understanding and interacting with both physical and social environments more coherently.
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Read at arXiv cs.AI