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+---
+title: "Dual-Channel Grounded World Modeling (DCGWM)"
+source_id: "arXiv:2606.18688v1"
+authors:
+  - "Akshay Hazare"
+affiliations: "Independent Researcher"
+date: 2026-06-17
+categories: ["cs.LG", "cs.AI"]
+note: "Position paper. Experimental validation in progress."
+url: "https://arxiv.org/abs/2606.18688v1"
+---
+
+# Dual-Channel Grounded World Modeling (DCGWM)
+
+**Authors**: Akshay Hazare (Independent)
+**arXiv**: 2606.18688v1 | **Date**: 2026-06-17
+**Categories**: cs.LG, cs.AI
+**Position paper — experimental validation ongoing**
+
+## 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)**: joint learning in a shared latent space causes the dominant channel to systematically collapse the subordinate channel's representational subspace, in a manner not resolvable by loss weighting alone.
+
+We propose **Dual-Channel Grounded World Modeling (DCGWM)**, designed to structurally prevent OIC through a partitioned latent space (Z_p ⊕ Z_b) with inward-only gradient flow. The Physical Grounding Channel updates only Z_p via VICReg-style alignment; the Social-Behavioral Grounding Channel updates only Z_b via alignment to emergent multi-agent simulation trajectories. An Inter-Channel Interface Module couples subspaces at the task level without cross-subspace gradients. An Asymmetric Grounding Adherence Loss penalizes rollout drift with a hard hinge for physical violations and a soft KL for behavioral divergence. A Generative Rendering Layer is architecturally isolated from the latent world model.
+
+Three theoretical results: the partition removes the gradient-interference pathway; each grounded subspace inherits anti-collapse guarantees; generative isolation is necessary under stated assumptions.
+
+## Key Contributions
+
+1. **Objective Interference Collapse**: Formalization of a new collapse mode — when two grounding signals with incompatible statistical structures share a latent space
+2. **DCGWM Architecture**: Partitioned latent space + inward-only gradient flow + separated grounding channels
+3. **Asymmetric Grounding Adherence Loss (L_AGA)**: First loss for rollout drift under heterogeneous grounding with incompatible tolerance structures
+4. **Isolation Necessity Theorem**: Under assumptions A1-A2, any α > 0 generative gradient causes world model drift
+5. **LLM World Modeling Critique**: NTP-trained LLMs face inherent subspace collapse that DCGWM avoids by design
+
+## Key Concepts
+
+- [[objective-interference-collapse|OIC]] — The new collapse mode this paper identifies
+- [[dcgwm|DCGWM]] — The architecture
+- [[inward-only-gradient-flow|Inward-Only Gradient Flow]] — The key separation mechanism
+- [[asymmetric-grounding-adherence-loss|L_AGA]] — Asymmetric rollout drift penalty
+- [[rollout-drift|Rollout Drift]] — Multi-step prediction error accumulation
+- [[isolation-necessity-theorem|Isolation Necessity]] — Formal generative isolation result