AI Memory Part 2 — Multi-Layer Density of Experts
Author: .ktg (ktg.one) Series: AI Memory
Note: Full article behind Medium/Cloudflare. Summary compiled from search snippets and the author’s own description.
Overview
Part 2 of the AI Memory series builds on Chain of Density (CoD) — previously framed as a context-extension protocol rather than merely a summarization trick. This installment turns CoD into something production-ready:
Multi-Layer Density of Experts (MLDoE) — a deployable framework that extends CoD compression into a Context Extension Protocol (CEP) designed to preserve LLM memory reliably in production.
Key Concepts
- MLDoE (Multi-Layer Density of Experts): Multiple density layers, each specialized (by “expert”), applied iteratively for high-fidelity compression
- CEP (Context Extension Protocol): The production variant — a standardized mechanism for extending LLM context windows beyond their native limits
- CoD (Chain of Density): The foundation from Part 1 — progressive density layering for machine-readable compression
Part 1 Recap
From ktg.one/blog:
- Chain of Density achieves 9.52/10 machine recall for fresh AI instances
- Progressive Density Layering (PDL) — iteratively compress with increasing density targets
- Cross-model tested on Claude, Grok-4
- The Carry Packet — structured context handoff between sessions
- Sets up the multi-expert approach explored in Part 2