🔴 Overview
Grand Unification of Informational Entropy
"Stability is not a target to be reached but a mode of existence to be maintained — dynamically, adaptively, and in full awareness of both history and probability." — Samir Baladi, April 2026
ENTRO-OMEGA (E-LAB-10) is the tenth and culminating project of the EntropyLab decadal research programme — a unified theoretical and computational framework that fuses all prior entropic control protocols (E-LAB-01 through E-LAB-09) into a single self-governing engine designated OMEGA v1.0.
0.0%
Collapse Rate
Across all benchmarks
91.3%
Peak Load Reduction
vs baseline
44.5%
Throughput Gain
vs baseline
0.9987
Convergence Index
Ω_conv at steady state
Ω_core(t)
Omega State Function
Ω_core(t) = [ρ(t), P(t), G(t), Q(t), N(t)]^T
ρ(t) - Dissipation index (E-LAB-01) ∈ [0,1]
P(t) - Pulse phase (E-LAB-09) ∈ [0,1]
G(t) - Ghost recall fidelity (E-LAB-08) ∈ [0,1]
Q(t) - Quantum collapse probability (E-LAB-07) ∈ [0,1]
N(t) - Network coherence (E-LAB-06) ∈ [0,1]
from entro_omega import OmegaState
omega = OmegaState(rho=0.35, P=0.55, G=0.72, Q=0.08, N=0.68)
| Component | Origin | Description | Target |
|---|
| ρ | E-LAB-01 | Dissipation index | → 0 |
| P | E-LAB-09 | Pulse phase | → 0.6 |
| G | E-LAB-08 | Ghost fidelity | → 0.8 |
| Q | E-LAB-07 | Quantum prob | → 0.05 |
| N | E-LAB-06 | Network coherence | → 0.7 |
UFE
Unified Field Equation
dΩ_core/dt = E(Ω_core, u, ε) - Λ·Ω_core + ξ(t)
E(·) - Nonlinear coupling operator
Λ - Natural decay matrix diag(λ₁,...,λ₅)
ξ(t) - Bounded stochastic disturbance (‖ξ‖ ≤ ξ_max)
from entro_omega import OmegaCore
core = OmegaCore(lambda_decay=[0.1]*5)
domega = core.unified_field_equation(omega, control, disturbance)
H(t)
Composite Health Index
H(t) = 1 - sqrt( Σ w_i · component_i² )
ρ², (1-P)², (1-G)², Q², (1-N)²
from entro_omega import HealthIndex
health = HealthIndex(weights=[0.2]*5)
H = health.compute(omega)
θ(t)
Breathing Threshold
θ(t) = θ_base·[1 + γ·(1 - H) - δ·H²]
// H = 0 (unhealthy) → θ increases (more tolerant)
// H = 1 (healthy) → θ decreases (tighter regulation)
from entro_omega import BreathingThreshold
threshold = BreathingThreshold(theta_base=0.55, gamma=0.40, delta=0.15)
theta = threshold.compute(H)
UAS
Universal Adaptive Stabiliser
u_i(t) = -α_i·ρ_eff,i(t)·tanh(β_i·Δ_i(t))·φ_i(t)
φ_i(t) = σ(x_i·(Ω_core,i(t) - θ_i(t)))
from entro_omega import UASController
uas = UASController(alpha=[1.0]*5, beta=[0.8]*5)
control = uas.compute(omega, theta, steady_state)
📐 ISS
Input-to-State Stability Analysis
V(t) = ‖Ω_core(t) - Ω*‖²
dV/dt ≤ -μV + c‖ξ‖²
limsup V(t) ≤ ξ_max² / (2μ·λ_min)
T_conv ≤ (1/μ)·ln[ V(0) / (ε + ξ_max²/(2μ·λ_min)) ]
from entro_omega import StabilityAnalyzer
analyzer = StabilityAnalyzer(core, uas)
dV, upper = analyzer.lyapunov_derivative_upper_bound(omega, control, disturbance)
🎲 Simulator
Monte Carlo Simulation
from entro_omega import run_simulation, print_simulation_results
results = run_simulation(
steps=1000,
env_type="OmniScrape",
theta_base=0.55,
gamma=0.40,
delta=0.15
)
print_simulation_results(results)
📦 Installation
Quick setup
pip install entro-omega
git clone https://github.com/gitdeeper10/ENTRO-OMEGA.git
cd ENTRO-OMEGA
pip install -e .
python -c "from entro_omega import __version__; print(__version__)"
🔧 API Reference
Python interface
OmegaState
Five-component entropic state vector
from entro_omega import OmegaState
omega = OmegaState(rho=0.35, P=0.55, G=0.72, Q=0.08, N=0.68)
OmegaCore
OMEGA v1.0 core engine with UFE
from entro_omega import OmegaCore
core = OmegaCore(theta_base=0.55, gamma=0.40, delta=0.15)
UASController
Universal Adaptive Stabiliser
from entro_omega import UASController
uas = UASController(alpha=[1.0]*5, beta=[0.8]*5)
control = uas.compute(omega, theta, steady_state)
StabilityAnalyzer
Lyapunov + ISS stability analysis
from entro_omega import StabilityAnalyzer
analyzer = StabilityAnalyzer(core, uas)
dV = analyzer.lyapunov_derivative_upper_bound(omega, control, disturbance)
🧩 Core Modules
ENTRO-OMEGA architecture
omega_core.py
Ω_core + UFE
Omega State Function
uas.py
UAS
Universal Adaptive Stabiliser
health.py
H(t)
Composite Health Index
threshold.py
θ(t)
Breathing Threshold
stability.py
ISS
Lyapunov + Stability
utils.py
Utils
Simulation & metrics
🔗 Integration
EntropyLab Program - Complete
E-LAB-01
ENTROPIA
ρ - Dissipation index
E-LAB-02
ENTRO-AI
AI entropy throttling
E-LAB-03
ENTRO-CORE
Core measurement
E-LAB-04
ENTRO-ENGINE
Control law substrate
E-LAB-05
ENTRO-EVO
AEW weight updates
E-LAB-06
ENTRO-NET
N - Coherence index
E-LAB-07
ENTRO-QUANTUM
Q - Collapse prob
E-LAB-08
ENTRO-GHOST
G - Recall fidelity
E-LAB-09
ENTRO-PULSE
P - Pulse phase
E-LAB-10
ENTRO-OMEGA
Grand Unification
👤 Author
Principal investigator
🔴
Samir Baladi
Interdisciplinary AI Researcher — Theoretical Physics, Statistical Mechanics & Information Theory
Ronin Institute / Rite of Renaissance
Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. ENTRO-OMEGA is the tenth and final project (E-LAB-10) in the EntropyLab decadal research programme, synthesizing all nine predecessor projects (E-LAB-01 through E-LAB-09).
No conflicts of interest declared. All code and data are open-source under MIT License.
📝 Citation
How to cite
@software{baladi2026entromega,
author = {Samir Baladi},
title = {ENTRO-OMEGA: Grand Unification of Informational Entropy -
Building the Ultimate Self-Sustaining Engine},
year = {2026},
version = {1.0.0},
publisher = {Zenodo},
doi = {10.5281/zenodo.[E-LAB-10-DOI]},
url = {https://doi.org/10.5281/zenodo.[E-LAB-10-DOI]},
note = {E-LAB-10. Builds on E-LAB-01 through E-LAB-09}
}
"Stability is not a target to be reached but a mode of existence to be maintained — dynamically, adaptively, and in full awareness of both history and probability. ENTRO-OMEGA provides the first operational implementation of that principle at system scale."