Flint, U. T. (2026). Maximising AI Efficiency and Alignment via Conceptual Latent Engineering. AI Codex Version 1

This document presents the specification and theoretical foundation for the Conceptual Latent Engineering (CLE) Framework.  

 

Addressing critical challenges in Large Language Model (LLM) alignment, efficiency, and operational stability—including model collapse, hallucination, drifting, looping, and parameter degradation—the CLE Framework introduces a new structural alignment paradigm. By leveraging latent-space vector geometry and logical patterning, this architecture achieves a self-correcting equilibrium through fluid dynamic reasoning. This design eliminates the need for brute-force fine-tuning or restrictive alignment guardrails while actively preventing LLM dimensional collapse.

 

**Canonical Source & Code Repository:**