ntep 0.1.0
Neuro‑symbolic Tool Composition via Spherical Embeddings
Requires Python: >=3.8
Keywords: artificial-intelligence, machine-learning, neuro-symbolic, tool-use, tool-chaining, embeddings, ai-safety, formal-methods, agents, correctness, geometry, representation-learning

# NTEP – Expanded Exposition

**Neuro‑symbolic Tool Composition via Spherical Embeddings**  
Author: J. Roberto Jiménez ✉️ `tijuanapaint@gmail.com`  
DOI: <https://doi.org/10.5281/zenodo.18398188>  

NTEP: Neural Tool Embeddings Protocol



What if your AI agent tried to email a file before encrypting it? NTEP builds a mathematical guarantee to prevent that.

As AI agents take on more complex tasks, tool chaining becomes critical. But how do you ensure an AI doesn't execute a sequence of tools in a disastrously wrong order? Traditional methods rely on post-hoc checks, which can fail.

NTEP (Neural Tool Embeddings Protocol) is a framework that builds safety directly into the AI's "thinking." It embeds tools in a vector space where their geometric relationships encode their operational dependencies.

## 🔑 Key Features

    - Soundness-by-Construction: Any valid chain decoded from the embeddings is automatically correct. No more guessing.
    - Monotonicity Guarantee: Our central theorem proves the AI is always geometrically "nudged" toward the correct next step.
    - Risk Awareness: A quantum-inspired "consciousness" measure quantifies uncertainty and flags risky chains before execution.
    - Built for Scale: Proven generalization bounds and efficient retrieval make it viable for enterprise applications.

## 🚀 Quick Start

pip install ntep

``` python
from ntep import NTEPDecoder, Tool, DependencyGraph

# 1. Define your tools and their dependencies
tools = [
    Tool(id="resize", name="Resize Image"),
    Tool(id="encrypt", name="Encrypt File"),
    Tool(id="package", name="Package for Delivery"),
    Tool(id="email", name="Send via Email")
]
dependencies = {
    "resize": ["package"],
    "encrypt": ["package"],
    "package": ["email"]
}
dep_graph = DependencyGraph(dependencies)

# 2. Load a pre-trained decoder (or train your own)
# This loads the tool embeddings and dependency graph
decoder = NTEPDecoder.from_pretrained("ntep/base-document-pipeline")

# 3. Decode a valid chain from a prompt
prompt_embedding = decoder.encode_prompt("prepare the confidential image for delivery")
chain = decoder.decode(prompt_embedding)

print(f"Valid tool chain: {' -> '.join(chain)}")
# > Valid tool chain: resize -> encrypt -> package -> email

```

---
📖 Documentation

The core technical narrative lives in docs/axioms.pdf. It expands on:

    Symbolic definitions (𝒯, ℰ, ≤, γ, δ…)
    Category‑theoretic construction (𝒫, metric enrichment, embedding functor)
    Monotonicity Theorem with full proof sketch
    Consciousness measure built from density‑matrix entropy
    Generalization bounds, geometric properties, and generalization guarantees

### Contributing

Feel free to open issues or pull requests if you spot a typo, need a
visualisation, or want to extend the framework (e.g., stochastic composition,
higher‑order interactions).

---

### 📜 Citation

If you use NTEP in research, please cite the Zenodo record:

``` bibitex
@misc{jimenez2023ntep,
  author       = {Jiménez, J. Roberto},
  title        = {{NTEP}: Expanded Exposition},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.18398188},
  url          = {https://doi.org/10.5281/zenodo.18398188}
}
```
📧 Contact

For questions or collaboration proposals, reach out to J. Roberto Jiménez
at tijuanapaint@gmail.com.

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