lnt-sovereign 1.0.4

High-performance neuro-symbolic verification layer for AI validation and logic-based guardrails.

LNT (Logic Neutrality Tool)

Experimental Neuro-Symbolic Validation Prototype

[!WARNING] Experimental Status: LNT is currently a research prototype in early-stage development. It has not been audited for security, has no third-party validation, and is not recommended for production use, especially in mission-critical financial or healthcare applications.

LNT is a developer library exploring the use of Neuro-Symbolic Validation to bridge probabilistic model outputs with structured logical constraints. It provides a technical framework for experimenting with formal verification in AI pipelines.

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Technical Concept

The core hypothesis of LNT is that business rules can be represented as a mathematical manifold and validated using an SMT solver (Z3) and vectorized matrix operations (NumPy).

Current Implementation Features (Alpha):

• Vectorized Logic Engine: A NumPy-based implementation for evaluating constraints.
• SMT Solver Integration: Experimental bridge to the Z3 solver for checking manifest consistency.
• Semantic Mapping: A lightweight fuzzy-matching layer (rapidfuzz) for mapping unstructured text to logic entities.
• Fairness Auditing: A basic implementation of the 80% rule for statistical parity analysis.

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Project Status

• Audits: None. This project has not undergone any formal security or logic audits.
• Production Use: None. There is no evidence of production deployment; it is intended for local experimentation and research.
• Benchmarks: Preliminary local benchmarks suggest sub-millisecond evaluation for simple rule sets, but these have not been independently verified or peer-reviewed.

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Exploration: Using the Validation Gate

# Developer install
pip install lnt-sovereign

Basic Example (Research Prototype)

from lnt_sovereign import LNTClient

# Initialize the local prototype
client = LNTClient(base_url="http://localhost:8000")

# Sample input for evaluation
proposal = {
    "amount": 450.0,
    "user_risk_score": 15,
}

# Evaluate against an experimental manifest
result = client.audit("test_policy", proposal)

print(f"Status: {result.status}")
print(f"Logic Health Score: {result.score}")

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Documentation (Work in Progress)

• Proposed Architecture
• Technical FAQ
• Roadmap & Evolution

LNT: A technical exploration in deterministic AI validation.