Language Construct Modeling (LCM) v1.13 – White Paper Repository

Language-Native Framework for Modular Semantic Control in LLMs

Title: Language Construct Modeling (LCM): A Language-Native Framework for Modular Semantic Control in LLMs
Author: Vincent Shing Hin Chong
Version: v1.13 Release Candidate
Status: Hash-sealed & Timestamped via OpenTimestamps
License: CC BY-SA 4.0 International
Main Theory DOI: 10.17605/OSF.IO/4FEAZ
Supplementary Theory DOI: 10.17605/OSF.IO/4FEAZ
ORCID: https://orcid.org/0009-0000-8198-6278
Contact: chonghin33@outlook.com (Open for collaboration)

📘 Overview

This repository hosts the official release of the white paper:

"Language Construct Modeling (LCM): A Language-Native Framework for Modular Semantic Control in LLMs"

LCM introduces a novel modular semantic control system designed for use within large language models (LLMs). It provides a recursive framework for prompt structuring and regenerative semantic behavior using a layered approach built on Meta Prompt Layering (MPL).

LCM does not require any internal modification to the language model. All structural logic, modular composition, control recursion, and regenerative behavior are implemented through language itself — via prompt-based layering and semantic orchestration.

This framework is fully language-native, requiring no fine-tuning, no scripting, and no API calls. It demonstrates that a large language model can act as a modular reasoning system purely through structured prompt logic.

The system supports stable semantic loops, dynamic control modules, and regenerative prompt trees (RPTs), making it suitable for scalable, interpretable prompt-based reasoning in LLMs.

Who knows what this system might contribute to the emergence of AI consciousness.

📁 Contents

• LCM-v1.13-whitepaper.pdf — Full white paper (core sections + appendices)
• cover.png — Official cover image
• LICENSE — CC BY-SA 4.0 License
• README.md — Project description

Appendices:

• Appendix A — Terminology
  Terminology of LCM framework
• Appendix B — Regenerative Prompt Tree Concept Chart
  Operational examples of Regenerative Meta Prompts
• Appendix C — Regenerative Prompt Tree (RPT) – Structural Definition and Theoretical Role Supplementary article

Theories:

-LCM Definition

-Strutural Gap supplemented by LCM

-Meta-prompt layering and Semantic Directive Prompt

⚖️ License

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
You may share and adapt this work, provided you give appropriate credit and distribute any derivative work under the same license.

© 2025 Vincent Shing Hin Chong

📚 Citation

If you wish to cite this white paper:

Chong, V. S. H. (2025, April 22). Language Construct Modeling (LCM): A Language-Native Framework for Modular Semantic Control in LLMs. https://doi.org/10.17605/OSF.IO/84TDB

All original documents, including the white paper, appendices and supplementary documents , are hash-sealed and timestamped via OpenTimestamps. This ensures verifiable authorship, integrity, and temporal priority of the entire Language Construct Modeling (LCM) v1.13 framework.

🙋 Questions & Contributions

This is a foundational release for layered prompt design in LLMs.
Feedback, replication efforts, and critical readings are welcome.

All forks and derivative works must retain the original author’s attribution and the license structure as specified.

License & Commercial Use Notice

This repository is licensed under CC BY-SA 4.0.

However, due to the structural nature of this work as a semantic framework, the following clause applies:

Semantic Commercial Use Clause Any commercial deployment or productization of this framework — or its derivative application as a service, system, or plugin — requires direct permission and licensing from the author.

This includes, but is not limited to: • Use in commercial SaaS tools • Integration into proprietary LLM-based agents • Paid educational or consultancy platforms • Any private modification not shared back under the same license

To request commercial use or collaboration, please contact:

Vincent Chong – chonghin33@outlook.com