👻 Overview
From discarded noise to actionable signals
"Systems that know where they have been can find their way back significantly faster than systems that do not." — Samir Baladi, April 2026
ENTRO-GHOST introduces the Entropic Memory Framework (EMF) that treats residual information — the thermodynamic traces left by prior computational states — as actionable signals rather than discarded noise. The framework achieves 47.3% reduction in recovery time following catastrophic entropy collapse events.
47.3%
Recovery Improvement
vs memoryless baseline
5
Core Modules
GhostTrace, GRA, VPD, HSP, Utils
100%
Test Pass Rate
All unit tests passing
86.3%
Max Improvement
With optimal parameters
Γ(t)
Ghost Trace
Γ(t) = ∫₀ᵗ Ψ(τ) · exp(−α(t − τ)) dτ
Γ[k] = exp(−α·Δt)·Γ[k−1] + (1 − exp(−α·Δt))·Ψ[k]
from entro_ghost import GhostTrace
gt = GhostTrace(alpha=0.1, dt=0.1)
gamma = gt.update(psi=0.95)
| Parameter | Description | Default |
|---|
| alpha | Temporal decay coefficient (forgetting rate) | 0.1 |
| dt | Sampling interval | 0.1 |
| memory_depth | Maximum number of history entries | 200 |
GRA
Ghost Recovery Algorithm
u_GRA(t) = u(t) + ζ · (Ψ*(t) − Γ(t))
ζ* = √(k_p · α) − α
from entro_ghost import GhostRecoveryOptimizer
gra = GhostRecoveryOptimizer(alpha=0.1, zeta=0.65)
result = gra.control(psi=0.85, psi_star=0.95, u_baseline=0.1)
🔍 VPD
Void Pattern Detector
E_V(t) = β · ∫_{V∩[t−w,t]} exp(−γ·(t−s)) ds
u_VGRA(t) = u_GRA(t) + κ · E_V(t) · sign(Ψ* − Ψ)
from entro_ghost import VoidPatternDetector
vpd = VoidPatternDetector(beta=1.0, gamma=0.05, kappa=0.3)
energy = vpd.update(observed=False)
💎 HSP
Holographic Stability Protocol
Ψ*_HSP(t) = median({Γᵢ(t) : i = 1, ..., M})
h = Φ · Ψ* (encode)
Ψ* ≈ Φᵀ(ΦΦᵀ)⁻¹·h (decode)
from entro_ghost import HolographicStabilityProtocol
hsp = HolographicStabilityProtocol(n_subsystems=8, encoding_depth=3)
result = hsp.step(psi_vector=[0.9, 0.85, ...], target=0.95)
📐 Stability
Routh-Hurwitz Stability Analysis
dε/dt = −(k_p + ζ)·ε(t) + ζ·δ(t)
dδ/dt = −α·δ(t) + α·ε(t)
λ² + (k_p + ζ + α)λ + (k_p·α) = 0
🎲 Simulator
Monte Carlo Simulation
from entro_ghost import run_simulation, print_simulation_results
results = run_simulation(
steps=1000,
alpha=0.1,
zeta=0.65,
use_vpd=True,
collapse_probability=0.03
)
print_simulation_results(results)
📦 Installation
Quick setup
pip install entro-ghost
git clone https://github.com/gitdeeper10/ENTRO-GHOST.git
cd ENTRO-GHOST
pip install -e .
python -c "from entro_ghost import __version__; print(__version__)"
🔧 API Reference
Python interface
GhostTrace
Exponentially-weighted integral of stability history
from entro_ghost import GhostTrace
gt = GhostTrace(alpha=0.1, dt=0.1, memory_depth=200)
gamma = gt.update(psi=0.95)
GhostRecoveryOptimizer
Ghost Recovery Algorithm with recall force
from entro_ghost import GhostRecoveryOptimizer
gra = GhostRecoveryOptimizer(alpha=0.1, zeta=0.65)
result = gra.control(psi=0.85, psi_star=0.95, u_baseline=0.1)
VoidPatternDetector
Treats informational gaps as latent potential energy
from entro_ghost import VoidPatternDetector
vpd = VoidPatternDetector(beta=1.0, gamma=0.05, kappa=0.3)
energy = vpd.update(observed=False)
HolographicStabilityProtocol
Distributed memory with Byzantine fault tolerance
from entro_ghost import HolographicStabilityProtocol
hsp = HolographicStabilityProtocol(n_subsystems=8, encoding_depth=3)
result = hsp.step(psi_vector, target=0.95)
🧩 Core Modules
ENTRO-GHOST architecture
ghost_trace.py
Γ(t)
Ghost Trace formalism
gra.py
GRA
Ghost Recovery Algorithm
vpd.py
VPD
Void Pattern Detector
hsp.py
HSP
Holographic Stability Protocol
utils.py
Utils
Simulation & metrics
👤 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-GHOST is the eighth project (E-LAB-08) in a ten-project research program, building directly on ENTROPIA (E-LAB-01) through ENTRO-QUANTUM (E-LAB-07).
No conflicts of interest declared. All code and data are open-source under MIT License.
📝 Citation
How to cite
@software{baladi2026entroghost,
author = {Samir Baladi},
title = {ENTRO-GHOST: Entropic Memory and Residual Pattern Discovery
in Informational Voids},
year = {2026},
version = {1.0.0},
publisher = {Zenodo},
doi = {10.5281/zenodo.19504584},
url = {https://doi.org/10.5281/zenodo.19504584},
note = {E-LAB-08. Builds on E-LAB-01 through E-LAB-07}
}
"Systems that know where they have been can find their way back significantly faster than systems that do not. Stability is not merely controlled; it is remembered, anticipated, and recovered from the residual intelligence left by the past."