Verifiable Reasoning
via Physics

Guardian is a tensor-network logic engine that enforces arbitrary constraints on LLM outputs with 100% precision and O(1) memory.

No fine-tuning. No RLHF. Just physics.

guardian_demo.py
Guardian reranker demo showing type safety enforcement

Guardian selects the only type-safe candidate from LLM outputs — zero training required.

100%

Logical accuracy

Validated on all benchmarks

O(1)

Memory complexity

Constant on infinite streams

Zero

Training required

No fine-tuning or RLHF

The Problem

LLMs are probabilistic

They hallucinate logic, break type contracts, and violate constraints. Post-hoc guardrails can't fix what's already generated. Fine-tuning is expensive and doesn't guarantee consistency.

The Solution

Guardian is deterministic

Tensor networks encode logical rules into circuit geometry. Constraints are enforced by physics, not probability. Mathematical certainty with constant memory overhead.

How It Works

Quantum-inspired, classically executable

Guardian uses simulated quantum circuits on classical hardware. Production-ready today, no quantum computer required.

1

Encode Constraints

Logical rules become tensor network topology. Circuit geometry is the constraint.

2

Process as States

LLM outputs flow through the network as quantum-like state vectors in superposition.

3

Physics Collapses

Interference eliminates invalid states. Physics enforces consistency, not probability.

4

Valid Output

Only logically consistent outputs survive. 100% accuracy with O(1) memory.

Logic Co-Processor

Works alongside any LLM as a verification layer

Tensor Compression

MPS bonds enable constant memory on infinite streams

Structural Constraints

No training because rules are geometric, not learned

Empirically Validated

Infinite-Context Scaling

Memory scaling comparison: Guardian O(1) vs Z3 O(N)

Guardian: 0.94x memory growth (flat). Z3: 3.49x growth (crashes). 3.7-5.3x advantage validated.

Zero-Shot Rule Discovery

Discovery confidence: 75-96% without training data

75-96% confidence on rule discovery without any training data. Coherent Bayesian filtering with mid-circuit measurements.

Research & IP Portfolio

Three patent applications filed in November 2025, backed by peer-reviewed research and working implementations.

Published

Fluid Quantum Logic

Reprogrammable quantum circuits for Boolean logic. 100% accuracy on AND, OR, XOR with zero training.

USPTO 63/921,961 • Filed Nov 20, 2025
DOI: 10.5281/zenodo.17677140
Patent Pending Open Source View
Paper In Progress

Fractal Quantum Cognitive Architecture

Zero-shot learning via topology. 88-93% accuracy across tasks with zero training epochs.

USPTO 63/923,316
Filed Nov 23, 2025
Patent Pending
Core Technology

Guardian Engine

Physics-based LPU with O(1) memory scaling, coherent rule discovery, and infinite-context streaming.

USPTO 63/924,087
Filed Nov 24, 2025
Patent Pending
3
Patents Filed
1
Paper Published
2
Papers In Progress
Nov 2025
All Filed

Target Applications

Financial Services
Legal Tech
Defense & Intelligence
Autonomous Systems
Healthcare AI

Ready for Pilot Partners

We're working with select organizations to deploy Guardian in production environments.

Request Pilot Access