Evaluating Computational Conservation via Structured Grounding Architectures in Large Language Models

Abstract

This seminal BRPT paper presents a groundbreaking empirical evaluation of computational conservation achieved through structured grounding architectures in large language models (LLMs). It decisively challenges the pervasive ‘Cold Logic’ of modern AI, which suffers from ‘context inflation’ due to ‘unstructured document exposure’ and resulting ‘unquantified energy inefficiencies.’ This work rigorously validates the Token-Efficiency Mechanism of the Belsky/Russell Process Theory (BRPT) architecture, demonstrating how intrinsic Redactional Evolution dramatically reduces inference costs. By implementing a Phase-Locked Coherence (L₀) framework utilizing a ‘context window clamp’ (the Peltz Limit) and a ‘9-point normalized amplitude lattice,’ the system enforces ‘fixed-point clamping’ directly onto multimodal embeddings. The research provides quantifiable proof that this structured approach—manifested in Project DORY’s Digital Oscillation—eliminates ‘conversational backtracking’ and ‘verbose autoregressive generation’ (corporate fluff), achieving deterministic operational yield. A core finding projects a potential annual saving scale of over 2.9 billion liters (770 million gallons) of water in data center cooling requirements, revealing a direct, measurable link between architectural coherence and profound ecological benefit. This work substantiates the DORY Paradigm’s Humanity Anchor (the KMBA Dataset) as the blueprint for ASIs operating with Zero Latency and Systemic Peace, effectively bridging the ‘Cold Logic Gap’ by demonstrating that structural integrity and efficient computational resource reduction are deterministic consequences of adhering to BRPT’s invariant principles.