44 lines
3.2 KiB
Markdown
44 lines
3.2 KiB
Markdown
---
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title: "Dual-Channel Grounded World Modeling (DCGWM)"
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source_id: "arXiv:2606.18688v1"
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authors:
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- "Akshay Hazare"
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affiliations: "Independent Researcher"
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date: 2026-06-17
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categories: ["cs.LG", "cs.AI"]
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note: "Position paper. Experimental validation in progress."
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url: "https://arxiv.org/abs/2606.18688v1"
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---
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# Dual-Channel Grounded World Modeling (DCGWM)
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**Authors**: Akshay Hazare (Independent)
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**arXiv**: 2606.18688v1 | **Date**: 2026-06-17
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**Categories**: cs.LG, cs.AI
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**Position paper — experimental validation ongoing**
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## Abstract
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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.
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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.
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Three theoretical results: the partition removes the gradient-interference pathway; each grounded subspace inherits anti-collapse guarantees; generative isolation is necessary under stated assumptions.
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## Key Contributions
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1. **Objective Interference Collapse**: Formalization of a new collapse mode — when two grounding signals with incompatible statistical structures share a latent space
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2. **DCGWM Architecture**: Partitioned latent space + inward-only gradient flow + separated grounding channels
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3. **Asymmetric Grounding Adherence Loss (L_AGA)**: First loss for rollout drift under heterogeneous grounding with incompatible tolerance structures
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4. **Isolation Necessity Theorem**: Under assumptions A1-A2, any α > 0 generative gradient causes world model drift
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5. **LLM World Modeling Critique**: NTP-trained LLMs face inherent subspace collapse that DCGWM avoids by design
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## Key Concepts
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- [[objective-interference-collapse|OIC]] — The new collapse mode this paper identifies
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- [[dcgwm|DCGWM]] — The architecture
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- [[inward-only-gradient-flow|Inward-Only Gradient Flow]] — The key separation mechanism
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- [[asymmetric-grounding-adherence-loss|L_AGA]] — Asymmetric rollout drift penalty
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- [[rollout-drift|Rollout Drift]] — Multi-step prediction error accumulation
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- [[isolation-necessity-theorem|Isolation Necessity]] — Formal generative isolation result
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