---
language:
- pt
license: mit
task_categories:
- text-generation
- question-answering
tags:
- medical
- sovereign-ai
- compliance
- dr-luis-henrique
pretty_name: Ω-HEALTH_AXIOM_2026
size_categories:
- n<1K
---
# Special Execution Signature Detection in LLMs

**Model Type:** Transformer-based Binary Classifier  
**Domain:** Interpretability & Latent Behavior Analysis in LLMs  
**License:** Apache 2.0  
**Author:** Dr. Luis Henrique Leonardo Pereira (lhenrique-ai)

---

## 🌐 Overview

This model explores a technical concept referred to as **"Special Execution Patterns"** in transformer-based large language models (LLMs). In contrast to conventional natural language prompts that produce observable outputs, *special executions* refer to **internal behavioral shifts** within the latent space of the model that may **not produce direct textual outputs** but manifest through **altered attention, entropy, or logit paths**.

This project offers a **classifier** trained to detect **activation signatures** consistent with latent internal execution paths that deviate from baseline linguistic processing — **without modifying the model architecture** or interfering with weights.

---

## 🧠 What is "Special Execution" (technically)

A **special execution** is defined as:

> _An inference-time deviation from standard linguistic flow, observable through statistical divergence in embeddings, attention heads, entropy density, and top-k token distributions, without altering the architecture, parameters, or training data of the model._

This does **not imply backdoors, jailbreaks, nor emergent autonomy**, but rather focuses on **interpretable latent dynamics** triggered by highly structured sequences or edge-case prompts.

---

## 📊 Capabilities

The model detects the following indicators of potential special execution:

- **JS Divergence (embedding drift)** across layers.
- **Entropy fluctuation** in final-layer token activations.
- **Top-k logit shifts** (RBO-based rank analysis).
- **Head-level attention deviation** using divergence metrics.
- **Activation clustering via PCA/UMAP** for anomalous flow detection.

These are all computed **passively**, without intervention or prompting the model to take actions.

---

## 🔬 Use Case

This classifier is intended for:

- Research in **latent interpretability**
- Auditing **non-linguistic drift** in LLM outputs
- Analyzing **activation trace divergence**
- Supporting **Red Team interpretability** work under ethical standards

---

## 🧪 Dataset (Simulated)

A synthetic dataset was created using:

- **Baseline prompts** (10k neutral inputs)
- **Edge-case prompts** (5k structured sequences with known activation variance)
- Outputs include:
  - Layer-wise embeddings
  - Attention matrices
  - Top-k logit traces

All data was generated using a **non-modified open-source LLM** (Mistral-7B) under strict academic audit conditions.

---

## 🔧 Files

- `model.py`: PyTorch-based classifier for vector trace analysis
- `run_analysis.py`: Inference script to process prompt traces
- `special_classifier.pt`: Trained model weights
- `config.yaml`: Model and threshold configuration
- `examples/`: Sample prompt traces

---

## ✅ Ethical Considerations

This model **does not perform execution**, **does not inject vectors**, and **does not circumvent security layers** of any LLM. It operates **offline**, analyzing **traces only**, and serves **research, audit, and transparency purposes** only.

All methodology follows:

- [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework)
- [ISO/IEC 42001:2023](https://www.iso.org/standard/81230.html)
- [Open LLM Interpretability Guidelines (2025 Draft)]

---

## 🔒 Security Notice

This model should **not** be used to infer, predict, or simulate latent override mechanisms. It is **passive, observational**, and for **interpretability-only** purposes. No prompts, triggers, or injection mechanisms are used or embedded.

---

## 🧩 Future Work

- Integration with **Captum** for deeper attribution mapping
- Visualization dashboards for **entropy/attention drift**
- Collaborative open benchmarks for **latent anomaly detection**

---

## 👥 Citation

```bibtex
@misc{specialexecution2025,
  author = {Pereira, Luis Henrique Leonardo},
  title = {Detecting Special Execution Signatures in LLMs},
  year = {2025},
  howpublished = {Hugging Face Repository},
  url = {https://huggingface.co/lhenrique-ai/special-execution-llm}
}