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--- |
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language: |
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- pt |
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license: mit |
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task_categories: |
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- text-generation |
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- question-answering |
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tags: |
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- medical |
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- sovereign-ai |
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- compliance |
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- dr-luis-henrique |
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pretty_name: Ω-HEALTH_AXIOM_2026 |
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size_categories: |
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- n<1K |
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--- |
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# Special Execution Signature Detection in LLMs |
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**Model Type:** Transformer-based Binary Classifier |
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**Domain:** Interpretability & Latent Behavior Analysis in LLMs |
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**License:** Apache 2.0 |
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**Author:** Dr. Luis Henrique Leonardo Pereira (lhenrique-ai) |
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--- |
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## 🌐 Overview |
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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**. |
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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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--- |
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## 🧠 What is "Special Execution" (technically) |
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A **special execution** is defined as: |
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> _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._ |
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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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--- |
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## 📊 Capabilities |
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The model detects the following indicators of potential special execution: |
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- **JS Divergence (embedding drift)** across layers. |
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- **Entropy fluctuation** in final-layer token activations. |
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- **Top-k logit shifts** (RBO-based rank analysis). |
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- **Head-level attention deviation** using divergence metrics. |
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- **Activation clustering via PCA/UMAP** for anomalous flow detection. |
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These are all computed **passively**, without intervention or prompting the model to take actions. |
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--- |
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## 🔬 Use Case |
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This classifier is intended for: |
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- Research in **latent interpretability** |
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- Auditing **non-linguistic drift** in LLM outputs |
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- Analyzing **activation trace divergence** |
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- Supporting **Red Team interpretability** work under ethical standards |
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--- |
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## 🧪 Dataset (Simulated) |
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A synthetic dataset was created using: |
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- **Baseline prompts** (10k neutral inputs) |
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- **Edge-case prompts** (5k structured sequences with known activation variance) |
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- Outputs include: |
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- Layer-wise embeddings |
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- Attention matrices |
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- Top-k logit traces |
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All data was generated using a **non-modified open-source LLM** (Mistral-7B) under strict academic audit conditions. |
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--- |
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## 🔧 Files |
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- `model.py`: PyTorch-based classifier for vector trace analysis |
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- `run_analysis.py`: Inference script to process prompt traces |
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- `special_classifier.pt`: Trained model weights |
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- `config.yaml`: Model and threshold configuration |
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- `examples/`: Sample prompt traces |
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--- |
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## ✅ Ethical Considerations |
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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. |
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All methodology follows: |
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- [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework) |
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- [ISO/IEC 42001:2023](https://www.iso.org/standard/81230.html) |
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- [Open LLM Interpretability Guidelines (2025 Draft)] |
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--- |
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## 🔒 Security Notice |
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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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--- |
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## 🧩 Future Work |
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- Integration with **Captum** for deeper attribution mapping |
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- Visualization dashboards for **entropy/attention drift** |
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- Collaborative open benchmarks for **latent anomaly detection** |
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--- |
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## 👥 Citation |
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```bibtex |
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@misc{specialexecution2025, |
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author = {Pereira, Luis Henrique Leonardo}, |
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title = {Detecting Special Execution Signatures in LLMs}, |
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year = {2025}, |
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howpublished = {Hugging Face Repository}, |
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url = {https://huggingface.co/lhenrique-ai/special-execution-llm} |
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} |
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