Skip to main content

ENTRO-ENGINE: Entropy Flow Regulator for Coupled Intelligence Systems

Project description

๐Ÿ”ด ENTRO-ENGINE โ€” Entropy Flow Regulator for Coupled Intelligence Systems

"A single engine regulates itself. A fleet of engines requires a coordinator." โ€” Samir Baladi, April 2026

ENTRO-ENGINE is the fourth project of the EntropyLab research program (E-LAB-04).
It advances from single-system entropy control to multi-system entropy coordination โ€” distributing thermodynamic pressure across coupled subsystems under a shared global budget constraint.


๐Ÿ“Œ DOI & Badges

DOI License: MIT Python 3.11+ PyPI Builds on Builds on Builds on Web Website Status


๐ŸŒ Official Website

https://entro-engine.netlify.app


๐ŸŽฏ Vision

Build a coordination layer for AI pipelines in which multiple entropy-generating subsystems share a common thermodynamic budget โ€” ensuring that no single subsystem collapses under load while others operate at idle, and that corrective pressure is distributed in proportion to each subsystem's entropy contribution.

The Core Shift

Architecture Scope Objective
ENTROPIA (E-LAB-01) Definition Establish ฮจ vocabulary
ENTRO-AI (E-LAB-02) Measurement Monitor & detect collapse
ENTRO-CORE (E-LAB-03) Local Control Stabilize single ฮจ field
ENTRO-ENGINE (E-LAB-04) Coordination Distribute pressure across coupled systems

๐Ÿ”ฌ Key Innovation

Instead of: Each subsystem independently minimizes its own entropy
ENTRO-ENGINE does: Enforce a global entropy budget across all coupled subsystems


ฮจ_total(t) = ฮจ_scraper + ฮจ_llm โ‰ค ฮจ_budget   โˆ€t

The engine computes corrective signals proportional to each subsystem's entropy fraction:


u_i(t) = โˆ’ฮฑ ยท ฯ_i(t) ยท tanh(ฮฒ ยท ฮ”(t))

Where:

  • ฯ_i = fractional entropy ratio (fairness principle)
  • ฮ”(t) = global entropy pressure above budget
  • tanh = saturation (prevents overcorrection)

๐Ÿ“Š Architecture


entro-engine/
โ”‚
โ”œโ”€โ”€ engine/                   # Core coordination engine
โ”‚   โ”œโ”€โ”€ controller.py         # ENTRO-ENGINE control law (Eq. 14)
โ”‚   โ”œโ”€โ”€ budget.py             # Entropy budget constraint (Eq. 9โ€“11)
โ”‚   โ””โ”€โ”€ allocator.py          # Proportional allocation ratios (Eq. 12โ€“13)
โ”‚
โ”œโ”€โ”€ subsystems/               # Coupled subsystem models
โ”‚   โ”œโ”€โ”€ scraper.py            # Scraper entropy source (Eq. 1, 4, 6)
โ”‚   โ”œโ”€โ”€ llm_queue.py          # LLM processing queue (Eq. 2, 5, 7)
โ”‚   โ””โ”€โ”€ base.py               # Abstract subsystem interface
โ”‚
โ”œโ”€โ”€ entropy/                  # Entropy formulation layer
โ”‚   โ”œโ”€โ”€ proxy.py              # Log-proxy functions: ฮจ = log(1 + x)
โ”‚   โ”œโ”€โ”€ aggregator.py         # Global ฮจ_total computation (Eq. 8)
โ”‚   โ””โ”€โ”€ normalizer.py         # Logistic normalization
โ”‚
โ”œโ”€โ”€ pipeline/                 # Realistic pipeline simulation
โ”‚   โ”œโ”€โ”€ simulator.py          # Three-phase pipeline (warmup / surge / recovery)
โ”‚   โ”œโ”€โ”€ simulator_v9.py       # Balanced regime engine
โ”‚   โ””โ”€โ”€ simulator_v10.py      # Recursive attractor engine
โ”‚
โ”œโ”€โ”€ reports/                  # Daily/Weekly/Monthly reports
โ”‚   โ”œโ”€โ”€ daily/                # Daily .txt reports
โ”‚   โ”œโ”€โ”€ weekly/               # Weekly summaries
โ”‚   โ”œโ”€โ”€ monthly/              # Monthly analyses
โ”‚   โ””โ”€โ”€ alerts/               # Alert logs
โ”‚
โ”œโ”€โ”€ tests/                    # Unit & integration tests
โ”‚   โ”œโ”€โ”€ test_controller.py    # Controller tests (9/9 passed)
โ”‚   โ”œโ”€โ”€ test_allocator.py     # Allocator tests
โ”‚   โ””โ”€โ”€ test_integration.py   # Integration tests
โ”‚
โ”œโ”€โ”€ data/                     # Simulation output datasets
โ”‚   โ””โ”€โ”€ simulation_runs/      # CSV trajectory files
โ”‚
โ”œโ”€โ”€ docs/                     # Documentation
โ”œโ”€โ”€ paper/                    # Research paper assets
โ”œโ”€โ”€ AUTHORS.md
โ”œโ”€โ”€ CHANGELOG.md
โ”œโ”€โ”€ LICENSE
โ”œโ”€โ”€ README.md
โ””โ”€โ”€ pyproject.toml


โš™๏ธ Core Equations

Eq. Expression Description
6 ฮจ_s(t) = log(1 + r_s(t)) Scraper entropy proxy
7 ฮจ_l(t) = log(1 + q_l(t)) LLM entropy proxy
8 ฮจ_total(t) = ฮจ_s + ฮจ_l Global entropy aggregate
9 ฮจ_total(t) โ‰ค ฮจ_budget โˆ€t Budget constraint
11 ฮ”(t) = ฮจ_total(t) โˆ’ ฮจ_budget Entropy pressure
12โ€“13 ฯ_i = ฮจ_i / ฮจ_total Proportional allocation ratios
14 u_i(t) = โˆ’ฮฑ ยท ฯ_i ยท tanh(ฮฒ ยท ฮ”) ENTRO-ENGINE control law

๐Ÿ“ฆ Installation

pip install entro-engine

From source:

git clone https://gitlab.com/gitdeeper10/entro-engine.git
cd entro-engine
pip install -e .

๐Ÿš€ Quick Start

from entro_engine import ENTROEngineController

# Initialize controller
controller = ENTROEngineController(alpha=0.5, beta=2.0, psi_budget=3.0)

# Compute control signal
signal = controller.compute_control(psi_i=1.5, psi_total=4.0)

print(f"u = {signal.u:.3f}")
print(f"ฯ = {signal.rho:.3f}")
print(f"ฮ” = {signal.delta:.3f}")

๐Ÿ“ˆ Simulation Results

Validated on a three-phase realistic pipeline (warmup โ†’ surge โ†’ recovery):

Metric Uncontrolled ENTRO-ENGINE Improvement Budget violations 312 / 1000 12 / 1000 96.2% reduction Peak ฮจ_total 5.84 3.31 43.3% reduction Peak LLM queue 87.6 req 23.4 req 73.3% reduction

Regime Distribution (v9 Final Run)

Regime Count Percentage ๐ŸŸข Stable 24 30.0% ๐ŸŸก Stress 19 23.75% ๐Ÿ”ด Critical 37 46.25%


๐Ÿ”— Links

Resource URL ๐ŸŒ Official Website https://entro-engine.netlify.app ๐Ÿ“Š Dashboard https://entro-engine.netlify.app/dashboard ๐Ÿ“š Documentation https://entro-engine.netlify.app/documentation ๐Ÿ“‹ Reports https://entro-engine.netlify.app/reports ๐ŸฆŠ GitLab (Primary) https://gitlab.com/gitdeeper10/entro-engine ๐Ÿ’ป GitHub (Mirror) https://github.com/gitdeeper10/entro-engine ๐Ÿ“ฆ PyPI https://pypi.org/project/entro-engine ๐Ÿ“Œ DOI https://doi.org/10.5281/zenodo.19441032 ๐Ÿ”ด ENTROPIA (E-LAB-01) https://doi.org/10.5281/zenodo.19416737 โš™๏ธ ENTRO-AI (E-LAB-02) https://doi.org/10.5281/zenodo.19284086 ๐Ÿง  ENTRO-CORE (E-LAB-03) https://doi.org/10.5281/zenodo.19431029 ๐Ÿ‘ค ORCID https://orcid.org/0009-0003-8903-0029


๐Ÿ—บ๏ธ EntropyLab Research Program

Project Title Status DOI E-LAB-01 ENTROPIA: Thermodynamic-Information Unification โœ… Published 10.5281/zenodo.19416737 E-LAB-02 ENTRO-AI: Entropy-Resistant AI Architectures โœ… Published 10.5281/zenodo.19284086 E-LAB-03 ENTRO-CORE: Self-Regulated Intelligence โœ… Published 10.5281/zenodo.19431029 E-LAB-04 ENTRO-ENGINE: Entropy Flow Regulator โœ… Published 10.5281/zenodo.19441032 E-LAB-05 ENTRO-FIN: Financial Market Entropic Signatures ๐Ÿ“‹ Planned โ€” E-LAB-06 ENTRO-SOCIAL: Social Network Information Cascades ๐Ÿ“‹ Planned โ€” E-LAB-07 ENTRO-QUANTUM: Quantum Extension ๐Ÿ“‹ Planned โ€” E-LAB-08 ENTRO-BIO: Biological Neural Networks ๐Ÿ“‹ Planned โ€” E-LAB-09 ENTRO-MANIFESTO: Unified Research Manifesto ๐Ÿ“‹ Planned โ€”


๐Ÿ“„ Citation

@software{baladi2026entroengine,
  author    = {Baladi, Samir},
  title     = {ENTRO-ENGINE: Entropy Flow Regulator for Coupled Intelligence Systems},
  year      = {2026},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.19441032},
  url       = {https://gitlab.com/gitdeeper10/entro-engine},
  note      = {E-LAB-04. Builds on E-LAB-01, E-LAB-02, E-LAB-03}
}

๐Ÿ“œ License

MIT License โ€” Copyright ยฉ 2026 Samir Baladi


Part of the EntropyLab ten-project research program "Intelligence by Design, Stability by Physics"

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

entro_engine-1.0.0.tar.gz (20.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

entro_engine-1.0.0-py3-none-any.whl (8.5 kB view details)

Uploaded Python 3

File details

Details for the file entro_engine-1.0.0.tar.gz.

File metadata

  • Download URL: entro_engine-1.0.0.tar.gz
  • Upload date:
  • Size: 20.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: ENTRO-ENGINE-Uploader/1.0

File hashes

Hashes for entro_engine-1.0.0.tar.gz
Algorithm Hash digest
SHA256 34443d7f25158090332a666be918f5004be7e204b7b66d345785394387666f6f
MD5 e24e03668b98b097cb94c1f7ac026406
BLAKE2b-256 0a66ecd74777ac1038d27a9498ed21853a9db349de0ee818ab6f5809b6a50531

See more details on using hashes here.

File details

Details for the file entro_engine-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: entro_engine-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 8.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: ENTRO-ENGINE-Uploader/1.0

File hashes

Hashes for entro_engine-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 319260710b74b9d985dddeaaf55c09da680c38b10a02fc1c3834fb14bfb20963
MD5 81cf96f740e87c108c857c9dc6c167e2
BLAKE2b-256 1ffcaafecba08d8f042c8258867c02fc9df0fb81a9bfcbbb408a68151b798164

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page