👻 ENTRO-GHOST (E-LAB-08)

Entropic Memory and Residual Pattern Discovery in Informational Voids

📖 Overview

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.

Key Contributions

Results

• 47.3% reduction in recovery time after catastrophic collapse
• Unconditional stability guarantees (Routh-Hurwitz)
• Byzantine fault tolerance up to ⌊(M-1)/2⌋ corrupted subsystems

🚀 Quick Install

pip install entro-ghost

🔬 Quick Start

from entro_ghost import GhostRecoveryOptimizer

# Initialize with default parameters
gra = GhostRecoveryOptimizer(alpha=0.1, zeta=0.65)

# Update ghost trace and get control signal
psi = 0.85      # current stability
psi_star = 0.95 # target stability
u_baseline = 0.1

result = gra.control(psi, psi_star, u_baseline)
print(f"Ghost pull: {result['u_ghost']:.3f}")
print(f"Total control: {result['u_total']:.3f}")

📚 Documentation

· Website: https://entro-ghost.netlify.app · Paper: DOI: 10.5281/zenodo.19504584 · API Docs: https://entro-ghost.readthedocs.io

🧬 EntropyLab Program

E-LAB Project Focus 01 ENTROPIA Theoretical foundations 02 ENTRO-AI AI inference stability 03 ENTRO-CORE Core measurement 04 ENTRO-ENGINE System coupling 05 ENTRO-EVO Adaptive weighting 06 ENTRO-NET Distributed sync 07 ENTRO-QUANTUM Probabilistic states 08 ENTRO-GHOST Entropic memory 09 (forthcoming) - 10 ENTRO-MANIFESTO Unified manifesto

📝 Citation

@software{baladi2026entroghost,
  author = {Samir Baladi},
  title = {ENTRO-GHOST: Entropic Memory and Residual Pattern Discovery},
  year = {2026},
  doi = {10.5281/zenodo.19504584},
  note = {E-LAB-08}
}

📄 License

MIT License © 2026 Samir Baladi

Part of the EntropyLab Research Program "Systems that know where they have been can find their way back significantly faster than systems that do not."