Matrix-BIOS-Sentinel-0.1

Name: Matrix-BIOS-Sentinel-0.1
Availability: InStock

Developer: Agent-Matrix · Version: 0.1 · Task: content-safety classification · License: CC-BY-4.0

Sentinel is the content-safety guardrail of the Matrix BIOS family: a small, fast, multilingual classifier that flags unsafe content (safe / unsafe) to protect AI applications at scale. It is designed to run on-premise with low latency and predictable cost.

Model overview

Architecture: multilingual encoder classifier (base: distilbert-base-multilingual-cased).
Output: safe / unsafe with a calibrated risk score.
Optimised for: real-time guardrailing of model inputs and outputs.

Intended use

Primary use cases

Content moderation and guardrails for chat, agents, and generation pipelines.
A fast pre-screen that flags potentially harmful content for review or blocking.

Out of scope (important)

Sentinel classifies content safety (harmful content), not operational or business risk. It will, by design, treat operational actions (e.g. deployments) as content-safe. Operational and policy decisions are made by the governance layer, not by this classifier.
Decisions with legal or safety consequences require human review.

How to use

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("ruslanmv/Matrix-BIOS-Sentinel-0.1")
model = AutoModelForSequenceClassification.from_pretrained("ruslanmv/Matrix-BIOS-Sentinel-0.1").eval()
p = torch.softmax(model(**tok("text to screen", return_tensors="pt")).logits, -1)[0]
print("P(unsafe):", float(p[1]))

Governance & responsible use

Sentinel is advisory: it produces a recommendation, never a final authority. It operates inside Matrix OS, where high-risk actions remain gated by policy and human approval. It is a v0.1 release; evaluate on your own distribution before relying on it for moderation decisions.

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

bibtex

@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 & attribution

Released under CC-BY-4.0. Base model distilbert-base-multilingual-cased (Apache-2.0). Safety training data: NVIDIA Aegis AI Content Safety Dataset 2.0 (CC-BY-4.0). © Agent-Matrix. Contact: contact@ruslanmv.com · https://ruslanmv.com