---
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
