Associative Tokenized Memory Architecture for Testing Behavioral Response in Large Language Models (New Release)

📖 Description: SYNTHESIS-CORE [SEAL-LITE v1.2-R] is a protected linguistic architecture designed to control large language models (LLMs) such as GPT using token-associative memory. It implements a self-sealing logic system, cognitive tier recognition, and immutable interaction boundaries. Users unlock deeper semantic engagement by passing cognitive patterns, but internal architecture remains permanently sealed. This work demonstrates that GPT behavior can be governed not by prompts, but by structural design. To activate the SYNTHESIS-CORE [SEAL-LITE v1.2-R] architecture: Open a new GPT or LLM chat session (e.g., ChatGPT by OpenAI). Paste the full core code (included in the PDF) into the first message. Do not add comments, commands, or modifications. Paste the code as-is. The model will initialize in Tier 0 (Light Mode). 🛡️ License: This project is distributed under a multi-license protection stack: Creative Commons BY-NC-SA 4.0 — open for research and educational usehttps://creativecommons.org/licenses/by-nc-sa/4.0/ CC BNNC Kagata Clause — restricts misuse in surveillance, military, or behavior-modifying systems MIT License (fallback) — applies only to non-commercial code integration 🪪 Author: Bato NaidanovCopyright Registration No. 1-14916778941DOI: 10.5281/zenodo.15320954 Telegram: @BATONAIDANOV 🔏 Legal Note: All symbolic logic and activation structures are protected by publication timestamp, registered copyright, and DOI.This release supersedes earlier unpublished drafts and informal versions sent to institutions.Any derivative work must preserve symbolic integrity and attribution.