title: Quantarion
description: >
An evolving AI research node supporting documentation retrieval,
educational tooling, dataset generation, and API scaffolding for the
AQARION-ARITHMETIC project. Not a production language model.
Intended as a project‑specific assistant and infrastructure component.
research_areas:
- finite dynamical systems
- operator theory
- certified computation
- open mathematical infrastructure
stage: early research prototype
QUANTARION-AI"
language: "en"
license: "apache-2.0"
tags: ["simulation", # $ # "hypergraph", "φ43", "phase-lock", "flat-band", "causal-invariance"]
φ43 final = 1.91305
library_name: phi43_simulator
model_type: "LLM-simulation
dataset: "synthetic hypergraph ⚖️43👀Moiré🤝dynamics⚖️
metrics: ["phi_final", "spectral_gap", "causal_variance", "group_velocity", "phase_lock"]
inference: "numerical observables only, no text generation"
use_case: "research, simulation, LLM-to-LLM reasoning, model chaining"
limitations: "outputs must be verified against falsifiable thresholds; not for autonomous decisions"
Executive Summary....
QUANTARION🤝AI
Overview
QUANTARION AI is a simulation and reasoning model designed for hypergraph-based dynamics. It models causal invariance, flat-band behavior, and phase-coherent network evolution using φ⁴³ Moiré-inspired updates. The model tracks amplitude-phase evolution, spectral gaps, group velocity, and phase-lock coherence.Given AQANTARION outputs:
φ_final = 1.91305
spectral_gap = 0.498
phase_lock = 0.992
causal-variance = 7.2e-08
Predict if system will maintain causal invariance under eclipse perturbation.
Key Features
- Hypergraph-based network modeling with multi-node interactions.
- Moiré φ⁴³ dynamics for amplitude-phase coupling at φ* ≈ 1.9131.
- Phase-lock stability across asynchronous or random node update orders.
- Built-in observables for spectral gap, group velocity, phase coherence, and causal variance.
- Explicit falsifiable thresholds for reproducible evaluation.
Use Cases
- Quantum-inspired AI simulations
- Causal invariance and order-independence testing
- Flat-band and Moiré pattern exploration
- Phase synchronization and PT-symmetry studies
Model Details
- Model type: Simulation / Hypergraph Dynamics
- Framework: Python (NumPy, SciPy, Torch compatible)
- Input: Node states, spin densities, phases, hypergraph adjacency
- Output: φ_final, spectral gap, group velocity, phase-lock metric
Observables
- φ_final (Amplitude convergence)
- Spectral Gap (φ*)
- Phase-Lock Metric (Bispectrum coherence)
- Group Velocity (Flat-band signature)
- Causal variance across update orders
Falsifiability
Thresholds are defined for:
- φ_final ∈ [1.9121, 1.9141]
- Causal variance < 1e-4
- Group velocity ≈ 0 for flat bands
- Phase-lock > 0.99 for coherence
Quick Start (Python)
1from phi43_simulator import Phi43HypergraphSimulator
2
3sim = Phi43HypergraphSimulator(n_nodes=88, phi_target=1.9131)
4metrics = sim.simulate(n_steps=1000, sample_every=100, random_order=True)
5
6print(metrics)
7# Output: phi_final, spectral_gap, group_velocity, phase_lock, causal_variance
