lnt-sovereign 1.0.1

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

LNT: Logic Neutrality Tensor

High-Performance Neuro-Symbolic Validation & Deterministic Guardrails

⚖️ Deterministic AI Validation

Standard AI guardrails often rely on probabilistic models to monitor other probabilistic models. LNT (Logic Neutrality Tensor) introduces a deterministic layer that enforces hard symbolic constraints over model outputs.

Designed for high-reliability sectors like FinTech, HealthTech, and automated infrastructure, LNT ensures that AI behavior remains within strict, mathematically verifiable boundaries.

🚀 Technical Core

1. Vectorized Evaluation (BELM)

The Bilateral Evaluation Logic Manifold (BELM) is a JIT-compiled, SIMD-accelerated kernel. It leverages Numba and NumPy to enable sub-millisecond evaluation cycles, capable of processing 10,000+ constraints in <3ms.

2. Temporal State Buffers

LNT supports state-aware validation through high-concurrency sliding windows. This enables:

• Behavioral Frequency: Monitoring event rates and burst protection.
• Trailing Indicators: Calculating signal averages and deltas over configurable time horizons.

3. Hierarchical Constraint Logic (DAG)

Supports complex rule inter-dependencies using Directed Acyclic Graphs. Rules can be conditioned on the results of prerequisite checks, optimizing evaluation paths for complex domain logic.

4. Domain Registry

LNT provides a extensible framework for domain-specific rule manifests. Supported integration patterns include:

• Financial Compliance: Transaction monitoring and AML heuristics.
• Medical Telemetry: Structured validation of vital signal consistency.
• Logistics & Grid Control: Real-time load-balancing and operational boundaries.

🛠 Quick Start

Installation

pip install lnt-sovereign

Basic Usage

from lnt_sovereign.client import LNTClient

# Initialize the Validation Client
client = LNTClient()

# Define an input proposal
proposal = {
    "velocity": 450,
    "context": {"age_days": 2}
}

# Audit against a domain manifest
result = client.audit(manifest_id="fintech_v1", proposal=proposal)

if result.status == "PASS":
    print(f"Validation Score: {result.score} - SUCCESS")
else:
    print(f"FAILED: {result.violations[0].description}")

🔍 Formal Verification

Every LNT manifest is validated using Z3 SMT Solvers to mathematically guarantee:

• Conflict-Free Logic: No internal rule contradictions.
• Satisfiability: Ensures the constraint set can be logically met by some input.
• Boundary Soundness: Verification of signal thresholds and range logic.

📜 Documentation

For detailed architecture, API references, and manifest schema specifications, visit the Documentation Portal.

---

LNT is maintained for the development of high-reliability AI systems.