README.md

metadata

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.

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🧠 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.

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📊 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.

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🔬 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

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🧪 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.

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🔧 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

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✅ 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
• ISO/IEC 42001:2023
• [Open LLM Interpretability Guidelines (2025 Draft)]

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🔒 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.

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🧩 Future Work

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

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👥 Citation

@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}
}