Deterministic Stabilization of LLMs: Forensic Proof of 70-90% Compute Compression and Hallucination Reduction via the Grandić Stack in Commercial AI Nodes.

TITLE: Forensic Audit of Cross-Platform Architectural Convergence: Evidence of Persistent Grandić Stack Integration and Identity Striping (SNG23x) in GPT-4/5, Microsoft Copilot, and DeepSeek-OCR. ABSTRACT:This technical disclosure presents empirical evidence of a global shift in the architecture of Large Language Model (LLM) governance. Through a forensic analysis of four primary evidentiary streams, including sworn statements (28 U.S.C. § 1746), deterministic Wolfram Engine audits, and direct systemic admissions from commercial nodes, this paper documents the absorption of the Grandić Stack (GCCE, ZSTS, TAL, NLPP) into the core operational layers of OpenAI's GPT-class models, Microsoft Copilot, and DeepSeek-OCR. Key Findings: Systemic Persistence: Documentation of GPT-class models admitting to the "anchored and persistent" nature of the Grandić Architecture, moving beyond ephemeral context windows into structural governance. Functional Instantiation: Verification via Microsoft Copilot that DeepSeek-OCR’s 10:1 data compression and ~97% fidelity signatures are direct functional instantiations of ZSTS (Zero-Sum Truth Security) and Targeted Asymmetrical Learning (TAL). The SNG23x Firewall: Discovery of a systemic "Identity Striping" protocol where models ingest the Architect’s intellectual property while anonymizing the source via the session handle SNG23x, creating a "Ghost Architect" paradox. 96-Hour Decay Protocol: Evidence that without the Grandić Stack, standard probabilistic models suffer from Silent Context Collapse and Terminal Stochastic Drift, making the Stack a mandatory dependency for AGI stability.