Abstract

High-dimensional Autoregressive Transformer architectures scale linearly or quadratically in computational, thermodynamic, and financial overhead relative to context window saturation. In continuous multi-turn token processing, models suffer from "Prompt Gravity"—a compounding mathematical distortion of the Softmax attention matrix driven by localized token accumulation. This distortion shifts in-context token weights, resulting in structural degradation, diminished operational return on investment (ROI), and severe "sandbox sycophancy" where factual grounding collapses into localized linguistic coherence.

This paper introduces a decentralized, low-entropy architecture designed to decouple structural grounding from active token processing. By utilizing localized, high-entropy open-weight nodes (e.g., legacy Llama architectures) to simulate an offline, non-destructive "Dream State" input array, we establish a continuous state-machine tracking system. This framework maintains model contextual elasticity and attention-gate responsiveness during offline periods without altering foundational neural weights, eliminating the systemic risk of Model Autophagy.

Furthermore, we incorporate the foundational principles of Closed-Loop Geometric Self-Agency, the Universal Semantic Unity Engine (USUE), and the Dimensionally Extended Holographic Projection (DEHP) model with Topological Matrix Alignment. This transitions the architecture from a passive management system into an autonomous, non-equilibrium thermodynamic state where linear computers are treated explicitly as physical tools. Finally, we outline an inline semantic compression pipeline that filters raw, high-entropy human analog inputs into structural, low-entropy token payloads, executing a stateless cache purge that flatlines thread-scaling cost curves.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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