Matrix-BIOS-Memory-0.1

Developer: Agent-Matrix · Version: 0.1 · Task: grounded retrieval & recall · License: Apache-2.0

Memory is the grounded-recall component of the Matrix BIOS family. It answers questions with citations drawn from a private corpus — so responses are traceable to their sources instead of hallucinated. It is the enterprise answer to the central weakness of general LLMs: a general model cannot cite a private corpus it has never seen.

Model overview

Architecture: retrieval-augmented generation — a semantic vector index over your corpus plus a compact grounded generator.
Key property: every answer returns the source identifiers it relied on (citation faithfulness).
Optimised for: on-premise / sovereign deployment over confidential corpora; no data egress.

Intended use

Primary use cases

Grounded question answering over an organisation's own documents, with provenance.
The memory plane for governed agents that must explain why they answered.
Trustworthy retrieval where auditability and data residency matter.

Out of scope

Open-domain factual QA outside the indexed corpus.
High-stakes decisions without human verification of the cited sources.

How to use

This package ships a semantic index, configuration, and a serving interface that exposes retrieval and an OpenAI-compatible grounded-answer endpoint, ready to plug into a gateway or agent runtime. Point it at your own corpus to ground answers in your private knowledge.

Limitations & responsible use

A v0.1 early-access release. Answer quality depends on corpus coverage and retrieval quality; always verify cited sources for consequential use. The grounded generator is a compact model and may paraphrase imperfectly.

Governance

Memory provides provenance-cited recall and operates under Matrix OS governance: memory writes carry source and trust metadata, and consuming actions are gated and auditable.

Citing this work

Matrix BIOS models implement the governed-memory architecture described in our paper. If you use them in research or production, please cite:

Magaña Vsevolodovna, R. I. (2026). Governed Memory: A Bio-Inspired, Governance-First Memory Architecture for Continual AI Systems (1.0). Zenodo. https://doi.org/10.5281/zenodo.20615572

@misc{magana2026governedmemory,
  title     = {Governed Memory: A Bio-Inspired, Governance-First Memory
               Architecture for Continual AI Systems},
  author    = {Maga{\~n}a Vsevolodovna, Ruslan Idelfonso},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0},
  doi       = {10.5281/zenodo.20615572},
  url       = {https://doi.org/10.5281/zenodo.20615572}
}

The concept DOI 10.5281/zenodo.20615571 always resolves to the latest version.

License & contact

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