entro-net 1.0.0

ENTRO-NET: Distributed Entropy Synchronization Protocols for Collective Neural Networks

🔴 ENTRO-NET — Distributed Entropy Synchronization Protocols for Collective Neural Networks

"Stability is not an individual property — it is a collective effort."
— Samir Baladi, April 2026

ENTROPY RESEARCH LAB · E-LAB-06 · v1.0.0

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📋 Overview

ENTRO-NET is the sixth project of the EntropyLab research program (E-LAB-06). It represents the leap from self-calibrating individual systems — mastered in ENTRO-EVO (E-LAB-05) — to distributed networked systems.

After successfully enabling a system to self-calibrate its weights via the Adaptive Entropy Weighting (AEW) algorithm with a 78.1% error reduction, this research builds a protocol that allows multiple nodes to physically share their stability states. The goal is to prevent cascading failure by synchronizing entropy flows across the network.

Extended empirical validation across N = 2 to N = 50 nodes reveals a non-trivial crossover from near-linear variance growth to a bounded saturation regime, with no catastrophic failure observed for any tested configuration.

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🎯 Core Innovations

Component	Description
Ψ-Sync Protocol	Real-time sharing of the entropy state Ψ(t) between nodes — stable nodes absorb informational pressure from stressed nodes
Collective-AEW	Extension of the single-node AEW algorithm: each node learns from both its own experience and the collective stability history of the entire network
θ_net Threshold	Dynamic networked threshold elevated from local to global level, ensuring the system responds as a coherent single entity
Fault Isolation	Automatic isolation of nodes exceeding Ψ_critical to prevent entropic contagion from propagating to stable regions

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📐 Mathematical Framework

Collective State:

Ψ_net(t) = { Ψ_1(t), Ψ_2(t), ..., Ψ_N(t) }

Entropy Synchronization Signal:

δ_i_sync(t) = κ · Σ_{j ≠ i} [ Ψ_j(t) − Ψ_i(t) ]

Collective-AEW Weight Update:

w_i(t+1) = w_i(t) − η · [ ∇L_local(t) + β · ∇L_collective(t) ]

Networked Threshold:

θ_net(t) = θ_base + γ · Var[ Ψ_net(t) ]

Global Lyapunov Stability Candidate:

V_net(t) = (1/2) · Σ_{i=1}^{N} [ Ψ_i(t) − Ψ_target ]²

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📊 Technical Objectives

Objective	Technical Description	Expected Outcome
Distributed Stability	Balance Ψ state across at least 3 distributed nodes	Reduce total entropy variance by > 50%
Networked Transfer	Instant transfer of optimal weights [w₁, w₂, w₃] between nodes	Reduce adaptation time for new nodes by > 70%
Fault Isolation	Isolate nodes exceeding Ψ_critical	100% protection for remaining network members

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📈 Scaling Results

Extended Analysis (N = 20, 30, 50)

Systematic experiments under the scraper regime (800 steps, 4 repetitions per N):

N	Variance (mean ± std)
20	0.165380 ± 0.002169
30	0.197713 ± 0.002204
50	0.221481 ± 0.000677

Comparison with Linear Extrapolation

Linear model fitted for N ≤ 15: σ² = 0.0101·N − 0.0331 (R² = 0.986)

N	Linear Prediction	Actual Variance	Deviation
20	0.1689	0.1654	−2.1%
30	0.2699	0.1977	−26.7%
50	0.4719	0.2215	−53.1%

Key finding: Linear scaling breaks down beyond N ≈ 20. The system enters a saturation regime where additional nodes contribute progressively less to global variance.

Scaling Curve

    0.25 ┤
         │                                    ★ N=50
    0.20 ┤                                ●
         │                            ●
         │                        ●
    0.15 ┤                    ●
         │                ●
         │            ●
    0.10 ┤        ●
         │    ●
         │●
    0.05 ┤●
         │
    0.00 ┼━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━→ N
         0    5    10   15   20   25   30   35   40   45   50

         ●  Empirical data points (mean variance)
         ──  Linear fit (N ≤ 15): σ² = 0.0101·N − 0.0331
         ──  Saturation fit: σ² = 0.228·(1 − e^{−N/16.2})
         ▒   Crossover region (N ≈ 15–25)

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🔬 Scaling Regimes

Regime	N Range	Behavior	Description
Linear Accumulation	2 – 15	σ² ≈ 0.0101·N − 0.0331	Near-linear growth, R² = 0.986
Transition	15 – 25	Bending toward saturation	Crossover zone
Saturation	25 – 50	σ² → 0.22	Variance ceiling observed

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📐 Proposed Saturation Model

σ²(N) = σ²_max · (1 − e^{−N/N₀})

Parameter	Symbol	Value	Interpretation
Saturation ceiling	σ²_max	0.228	Maximum variance asymptote
Characteristic scale	N₀	16.2	Crossover scale (nodes)
Goodness of fit	R²	0.992	—
Root mean square error	RMSE	0.004	—

Asymptotic properties:

• For small N: σ² ≈ (σ²_max / N₀) · N → linear growth
• For large N: σ² → σ²_max → bounded variance

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🧠 Key Scientific Insights

1. No Catastrophic Failure
The system remains stable and operational for all tested configurations (N ≤ 50). Variance does not diverge.

2. Intrinsic Self-Regulation
Variance growth is actively constrained by three emergent internal mechanisms:

• Adaptive aggression auto-tuning (α self-adjusts)
• Collective-AEW weight redistribution
• Networked threshold elevation (θ_net)

3. Smooth Crossover
The transition from linear growth to saturation is gradual — a soft scaling crossover rather than a sharp phase transition.

4. Bounded Variance Ceiling
The system approaches a natural ceiling σ² ≈ 0.23, independent of further node addition beyond N ≈ 30.

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🚀 Practical Recommendations

Use Case	Recommended N	Expected Variance	Reliability
Production (critical)	2 – 5	< 0.05	🟢 Excellent
Production (standard)	6 – 12	0.05 – 0.09	🟢 Good
Experimental	13 – 20	0.09 – 0.17	🟡 Acceptable
Research / Development	21 – 30	0.17 – 0.20	🔴 Degraded
Not recommended	> 30	> 0.20	⚠️ Saturated

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📁 Project Structure

ENTRO-NET/
│
├── entro_net/                  # Core library
│   ├── __init__.py
│   ├── psi_sync.py             # Ψ-Sync protocol
│   ├── collective_aew.py       # Collective-AEW optimizer
│   ├── net_threshold.py        # θ_net dynamic threshold
│   ├── fault_isolation.py      # Cascading failure prevention
│   └── simulator.py            # Distributed simulation engine
│
├── bin/                        # Executables
│   └── run_simulation.py
│
├── tests/                      # Unit and integration tests
├── examples/                   # Usage examples
├── scripts/                    # Utility scripts
├── docs/                       # Documentation
├── results/                    # Simulation outputs
└── Netlify/                    # Static website

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⚡ Quick Start

from entro_net import PsiSync, CollectiveAEW, NetThreshold

# Initialize 3-node network
sync       = PsiSync(n_nodes=3)
collective = CollectiveAEW(eta=0.01, target=0.339)
threshold  = NetThreshold(theta_base=1.2)

# Run distributed control loop
for t in range(500):
    psi_states = [node.observe() for node in nodes]

    # Synchronize entropy states across network
    synced_psi = sync.broadcast(psi_states)

    # Collective weight adaptation
    weights = collective.step(synced_psi)

    # Apply global networked threshold
    theta = threshold.update(synced_psi)

    # Isolate faulty nodes if needed
    if sync.detect_fault(psi_states):
        sync.isolate_node(faulty_id)

Reproduce all experiments:

python bin/run_simulation.py \
  --nodes N \
  --steps 800 \
  --regime scraper \
  --repeats 4

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🔗 Roadmap Integration

Project	Code	Contribution to ENTRO-NET
ENTROPIA	E-LAB-01	Unified Dissipation State Function — foundational entropy formalism
ENTRO-AI	E-LAB-02	AI risk monitoring — dynamic entropy threshold design
ENTRO-CORE	E-LAB-03	Singular system will — local AEW weight architecture
ENTRO-ENGINE	E-LAB-04	Budget distribution between coupled systems
ENTRO-EVO	E-LAB-05	Self-learning AEW — 78.1% error reduction baseline
ENTRO-NET	E-LAB-06	Collective Ψ-Sync — distributed stability (this work)

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📚 Links & Resources

Resource	URL
📄 Paper (Zenodo)	10.5281/zenodo.19474217
📋 OSF Preregistration	10.17605/OSF.IO/9Y7RX
💻 GitLab	gitlab.com/gitdeeper10/ENTRO-NET
💻 GitHub	github.com/gitdeeper10/ENTRO-NET
💻 Bitbucket	bitbucket.org/gitdeeper-10/entro-net
💻 Codeberg	codeberg.org/gitdeeper10/entro-net
📦 PyPI	pypi.org/project/entro-net
🌐 Website	entro-net.netlify.app

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📝 Citation

@software{baladi2026entronet,
  author    = {Baladi, Samir},
  title     = {ENTRO-NET: Distributed Entropy Synchronization Protocols
               for Collective Neural Networks},
  year      = {2026},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.19474217},
  url       = {https://github.com/gitdeeper10/ENTRO-NET},
  note      = {E-LAB-06. Builds on E-LAB-01 through E-LAB-05.
               EntropyLab Research Program.
               OSF Preregistration: 10.17605/OSF.IO/9Y7RX}
}

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👤 Author

Samir Baladi
Interdisciplinary AI & Theoretical Physics Researcher
Ronin Institute / Rite of Renaissance

• 📧 [e-mail]
• 🆔 ORCID: 0009-0003-8903-0029
• 💻 GitLab / GitHub / Codeberg: @gitdeeper10

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📄 License

MIT License — see LICENSE file for details.

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Part of the EntropyLab ten-project research program · E-LAB-06 ✅ Complete

"Intelligence by Design, Stability by Physics, Evolution by Learning, Harmony by Network"