NLDA MK-II Memory: Native Embedded Weights and Tensors

Author: Winsock

Publication ID: ORDL-0002

ORDL NLDA MK-II-M | Memory Architecture

Extend the MK-II base with native embedded memory weights and tensors, enabling persistent context recall without external vector databases or key-value stores.

MK-II Base runtime, memory tensor allocator (static pools), weight serialization format v2.1.1-memory, context window expansion module.

Memory weights are stored as additional transformer layers with fixed-size tensor slots. Query-time recall routes through a dedicated attention head trained for semantic similarity over embedded vectors. No external network calls required.

Context recall accuracy 94.7% on synthetic benchmarks with 10K embedded memories. Latency overhead <8ms per memory query. Memory weights compress to 32M parameters with minimal recall degradation.

Memory model achieves persistent recall across 1000+ session turns without re-prompting. All memory operations are deterministic and reproducible.

By embedding memory directly into the model weights, we eliminate the latency and privacy risks of external vector stores. The tensor-weighted retrieval mechanism is novel and patent-pending.

NLDA MK-II Memory provides native embedded context persistence at inference speed. Suitable for long-running agents, personal assistants, and institutional knowledge bases.

First transformer with native embedded memory weights. Eliminates external retrieval infrastructure. Enables fully offline, air-gapped memory systems.