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Resonance-based, symbolically mirrored coupling of heterogeneous NN models in the AeonLayer for AeonAI (GenesisAeon project, CREP + Sigillin from unified-mandala)
Resonance-based symbolic coupling of heterogeneous neural network models in the AeonLayer
Version 0.1.0 – GenesisAeon Project DOI: 10.5281/zenodo.19110330 Zenodo Record: https://zenodo.org/records/19110330
Resonance-based, symbolically mirrored coupling of heterogeneous neural network models (Transformer, CNN, RNN, GraphNN, Spiking) in the AeonLayer for AeonAI. Built on CREP + Sigillin from unified-mandala.
The central formula of the GenesisAeon stack:
$$ L_{\text{Aeon}} = \sum_i w_i \cdot M_i \cdot \sigma!\left(\beta(R_i - \Theta)\right) $$
| Symbol | Meaning |
|---|---|
| $w_i$ | Dynamic resonance weight for model $i$ (from WeightingEngine) |
| $M_i$ | Mirror-Matrix $M_i = P_i \Phi_{\text{sigil}} P_i^\top$ (from SymbolicMirror) |
| $R_i$ | Raw resonance signal for model $i$ (adapter-specific) |
| $\Theta$ | Global resonance threshold |
| $\beta$ | Sharpness / inverse temperature |
| $\sigma$ | Logistic sigmoid |
Area regime ($S \propto A$):
$$ w_i^{(A)} = \frac{\exp(-\lambda H_i)}{\sum_j \exp(-\lambda H_j)} $$
Volume regime ($S \propto V$, dimension $d$):
$$ w_i^{(V)} = \frac{\exp!\left(-\lambda H_i^{d/2}\right)}{\sum_j \exp!\left(-\lambda H_j^{d/2}\right)} $$
$$ u_i = \sigma!\left(\kappa(C_i - \tau)\right) $$
$$ \mathrm{CREP}i = \left(1 - \frac{H_i}{H{\max}}\right) \cdot \rho_i \cdot u_i $$
$$ M_i = P_i , \Phi_{\text{sigil}} , P_i^\top, \qquad [\Phi_{\text{sigil}}]_{jk} \propto e^{-\frac{1}{2}|j-k|} $$
$$ E = | L_{\text{Aeon}} |_F $$
pip install advanced-weighting-systems
pip install "advanced-weighting-systems[stack]"
Installs: mirror-machine, entropy-governance, sigillin, mandala-visualizer,
utac-core, cosmic-web.
git clone https://github.com/GenesisAeon/AdvancedWeightingSystems
cd AdvancedWeightingSystems
pip install -e ".[dev]"
import numpy as np
from advanced_weighting_systems.aeon_layer import AeonLayerConfig
from advanced_weighting_systems.symbolic_mirror import (
TransformerAdapter, CNNAdapter, RNNAdapter, GraphNNAdapter
)
from advanced_weighting_systems.weighting_engine import WeightingConfig
from advanced_weighting_systems.models.coupling import ResonanceCoupling
DIM = 16
coupling = ResonanceCoupling(
adapters=[
TransformerAdapter(dim=DIM),
CNNAdapter(dim=DIM),
RNNAdapter(dim=DIM),
GraphNNAdapter(dim=DIM),
],
dim=DIM,
aeon_config=AeonLayerConfig(beta=1.5, theta=0.0, n_models=4),
weighting_config=WeightingConfig(lam=1.0),
)
rng = np.random.default_rng(0)
activations = [rng.standard_normal(DIM) for _ in range(4)]
entropies = rng.uniform(0.1, 0.9, size=4)
coherences = rng.uniform(0.3, 0.8, size=4)
rho = rng.uniform(0.4, 1.0, size=4)
output = coupling.step(activations, entropies, coherences, rho)
print(f"L_Aeon shape : {output.aeon_state.layer_output.shape}")
print(f"Resonance energy : {output.energy:.6f}")
print(f"CREP scores : {output.weighting_result.crep_scores}")
# Couple four adapter types with AeonLayer + mandala ASCII visualisation
aws couple --models trans,cnn,rnn,graph --aeon-layer --entropy 0.4 --visualize
# Compute resonance weights for named models with CREP bar chart
aws weight --model-ids model_a,model_b,model_c --regime volume --lam 2.0 --visualize
advanced_weighting_systems/
├── aeon_layer.py # L_Aeon aggregation (AeonLayer, AeonLayerConfig)
├── weighting_engine.py # Entropy-governance, UTAC, CREP (WeightingEngine)
├── symbolic_mirror.py # Sigillin + MandalaMap -> M_i (SymbolicMirror, adapters)
├── cli.py # Typer CLI (aws couple / aws weight)
├── models/
│ ├── __init__.py # Re-exports all adapters
│ └── coupling.py # ResonanceCoupling end-to-end pipeline
└── utils/
├── __init__.py
└── entropy.py # Shannon entropy, EntropyTable, governance weights
| Adapter | --models key | Resonance Signal |
|---|---|---|
TransformerAdapter | trans | mean(tanh(activations)) |
CNNAdapter | cnn | max(abs(activations)) |
RNNAdapter | rnn | std(activations) |
GraphNNAdapter | graph | mean(activations^2) |
SpikeAdapter | spike | spike rate above threshold |
pytest # run all tests with coverage
pytest --cov-report=html # HTML coverage report
Coverage target: 99 %+ (enforced via --cov-fail-under=99).
Contract tests verify interface compatibility with:
mirror-machine — Mirror-Matrix shape and finite-value contractsentropy-governance — monotonicity of weights w.r.t. entropyutac-core — UTAC gate range and monotonicity w.r.t. coherencemkdocs serve # local preview
mkdocs build --strict # production build (zero warnings)
Published at: https://genesisaeon.github.io/AdvancedWeightingSystems
@software{genesisaeon_aws_2026,
author = {GenesisAeon},
title = {Advanced Weighting Systems: Resonance-based Coupling of Heterogeneous NN Models},
year = {2026},
version = {0.1.0},
doi = {10.5281/zenodo.19110330},
url = {https://github.com/GenesisAeon/AdvancedWeightingSystems}
}
Dual-licensed:
src/, tests/, scripts, configuration) — GPL-3.0-or-laterdocs/, RELEASE_GUIDE.md, CONTRIBUTING.md, CHANGELOG.md, templates) — CC BY 4.0See LICENSE for the full split. — GenesisAeon Project, 2026.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 1.0.0 | 28 Jun 2026 | 21 |
This describes our collected data; it is not a security assessment.
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Observed since 20 Oct, 19:59 UTC
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package:f64a51fec6cb4d6bba939d4b50c7244d17c0be4d6daf1bb1bad838c58c8ec666