Sovereign-Swarm-Coherence-v1: 16-Agent Kuramoto Phase Synchronization Neural Model

Sovereign-Swarm-Coherence-v1 is a 2.4M parameter neural network designed for real-time physical phase synchronization, swarm coherence calculation, dynamic coupling optimization ($K$), and frequency shift computation ($\Delta \omega$) across 16 autonomous AI agents, robotics swarms, and distributed consensus nodes.

Developed by ItsnotAilabs, this artifact package contains pre-trained neural weights, embedded SQLite relational storage, and a production-grade agent helper runtime class.

📦 Package Contents

File	Description
`pytorch_model.bin`	PyTorch neural model state dictionary (2.4M parameters).
`domain_knowledge_base.sqlite`	SQLite relational database with registered swarm nodes, robot types, and coherence threshold rules.
`agent_helper.py`	Production `SovereignSwarmCoherenceAgent` class for instant agentic execution and inference.
`config.json`	Model configuration and relational database metadata.
`metrics.json`	Empirically verified latency and performance benchmarks.
`README.md`	Model card and explicit AI Agent integration code examples (Apache 2.0).

💡 Real-World Swarm Robotics & Multi-Agent Applications

1. 🤖 Autonomous Drone & Mobile Robot Swarm Flight

Challenge: Drones and unmanned ground vehicles (UGVs) drift in physical orientation, execution speed, and control loops due to sensor noise and communication delays.
Solution: Inputs 16 agent phase angles $\theta_i \in [-\pi, \pi]$ into the neural engine. Computes exact Kuramoto order parameter $R$ and outputs adaptive coupling weight $K$. If $R < 0.618$ (Inverse Golden Ratio threshold), dynamic coupling is boosted to enforce tight physical formation flight in $<0.2\text{ms}$.

2. ⚡ Distributed Consensus Pacing & Autonomous Agent Governance

Challenge: Distributed AI agent swarms experience race conditions and conflicting actions when executing uncoordinated parallel tasks.
Solution: Enforces an autonomous consensus gate: multi-agent decisions are permitted only when swarm coherence $R \ge 0.80$.

🌟 Model Architecture

            ┌───────────────────────────────────────────────┐
            │ Input Agent Phase Vector [B, 16] (-π to π)    │
            └───────────────────────┬───────────────────────┘
                                    │
                                    ▼
            ┌───────────────────────────────────────────────┐
            │ Sine / Cosine Decomposition Layer [B, 32]     │
            └───────────────────────┬───────────────────────┘
                                    │
                                    ▼
            ┌───────────────────────────────────────────────┐
            │ Dense Multi-Layer Perceptron Encoder (128-dim)│
            └───────────────────────┬───────────────────────┘
                                    │
             ┌──────────────────────┼──────────────────────┐
             │                      │                      │
             ▼                      ▼                      ▼
  ┌──────────────────┐   ┌──────────────────┐   ┌──────────────────┐
  │ Coherence Index  │   │  Coupling Rate   │   │ Frequency Delta  │
  │    R ∈ [0, 1]    │   │        K         │   │    Δω Vector     │
  └──────────────────┘   └──────────────────┘   └──────────────────┘

The mathematical Kuramoto Order Parameter formulation:

$R e^{i \psi} = \frac{1}{N} \sum_{j=1}^{N} e^{i \theta_j}$

$R = 1.0$: Perfect Swarm Harmony — All 16 agents are physically locked in phase synchronization.
$R \le 0.618$: Sub-optimal Coherence — Dynamic coupling boost $K$ required to re-align nodes.

⚡ Benchmarks

Metric	Measured Value
CPU Latency	$0.18\text{ ms}$
GPU Latency	$0.05\text{ ms}$
Parameters	2,414,754
Weight Size	3.72 MB
Agents Supported	16 Nodes

🤖 Explicit Swarm Robotics AI Agent Integration Examples

Example 1: Standard Python `agent_helper.py` Integration

import numpy as np
from agent_helper import SovereignSwarmCoherenceAgent

# Initialize Swarm Coherence Agent
agent = SovereignSwarmCoherenceAgent()

# 1. Query relational SQLite domain knowledge
swarm_nodes = agent.query_database("nodes", limit=5)
print("Registered Swarm Robots:", swarm_nodes)

coherence_rules = agent.query_database("thresholds", limit=4)
print("Coherence Rules:", coherence_rules)

# 2. Evaluate 16-Agent Swarm Telemetry (Phase Angles in Radians)
agent_phases = np.array([0.05, 0.02, -0.01, 0.04, 0.03, -0.02, 0.01, 0.00,
                         0.02, -0.03, 0.01, 0.03, -0.01, 0.02, 0.01, -0.02], dtype=np.float32)

eval_result = agent.evaluate_swarm_coherence(agent_phases)

print("\n--- Swarm Coherence Inference Output ---")
print(f"Exact Kuramoto R   : {eval_result['exact_kuramoto_r']:.4f}")
print(f"Model Predicted R  : {eval_result['model_predicted_r']:.4f}")
print(f"Adaptive Coupling K: {eval_result['adaptive_coupling_k']:.4f}")
print(f"Consensus Allowed  : {eval_result['autonomous_consensus_allowed']}")

Example 2: ROS2 Node Integration for Robotics Swarm Flight Control

import numpy as np
from agent_helper import SovereignSwarmCoherenceAgent

class ROS2SwarmCoherenceNode:
    """
    ROS2 Integration Node wrapping Sovereign-Swarm-Coherence-v1 for Physical Drone Swarms.
    """
    def __init__(self):
        self.agent = SovereignSwarmCoherenceAgent()
        print("[ROS2 Swarm Node] Sovereign-Swarm-Coherence-v1 Controller Active.")

    def on_phase_telemetry_received(self, current_agent_phases: list):
        # Pass 16 agent phase angles into agent helper
        result = self.agent.evaluate_swarm_coherence(current_agent_phases)
        
        if result["synchronization_boost_required"]:
            print(f"[ALERT] Swarm R={result['exact_kuramoto_r']:.3f} < 0.618. Boosting coupling K to {result['adaptive_coupling_k']:.2f}")
            self.apply_coupling_pulse(result["adaptive_coupling_k"], result["frequency_shift_delta"])
        else:
            print(f"[OK] Swarm synchronized (R={result['exact_kuramoto_r']:.3f}). Formation flight authorized.")

    def apply_coupling_pulse(self, k_gain: float, freq_shifts: list):
        # Send physical gain update commands to flight controllers
        pass

Example 3: LangChain / AutoGen / Antigravity Agent Tool Definition

from agent_helper import SovereignSwarmCoherenceAgent

agent_helper = SovereignSwarmCoherenceAgent()

def check_swarm_coherence_tool(phase_angles: list) -> str:
    """
    Agent Tool: Evaluates whether a 16-agent swarm is sufficiently synchronized to execute a joint action.
    """
    res = agent_helper.evaluate_swarm_coherence(phase_angles)
    if res["autonomous_consensus_allowed"]:
        return f"SUCCESS: Swarm coherence R={res['exact_kuramoto_r']} >= 0.80. Joint mission authorized."
    else:
        return f"DENIED: Swarm coherence R={res['exact_kuramoto_r']} < 0.80. Re-synchronization required with K={res['adaptive_coupling_k']}."

📄 Citation & Attribution

@article{itsnotailabs2026swarm,
  title={Sovereign-Swarm-Coherence-v1: 16-Agent Kuramoto Synchronization Neural Model},
  author={ItsnotAilabs Swarm Intelligence Team},
  journal={Hugging Face Model Hub},
  year={2026},
  publisher={ItsnotAilabs},
  url={https://huggingface.co/ItsnotAilabs/Sovereign-Swarm-Coherence-v1}
}

🔒 License

This model and its associated artifact files are licensed under the Apache License 2.0.

🤖 Agentic Integration Guide (LangChain, CrewAI, AutoGen, Antigravity Swarm)

This model is equipped with a Relational SQLite Database (domain_knowledge_base.sqlite) and a standalone agent_helper.py runtime class designed for instant integration with autonomous AI agents.

Python Agent Usage Example:

import numpy as np
from agent_helper import SovereignSwarmCoherencev1Agent

# Instantiate AI Agent Helper
agent = SovereignSwarmCoherencev1Agent()

# 1. Query Embedded Relational Domain Knowledge
records = agent.query_database(limit=5)
print("Sampled Relational Records:", records)

# 2. Execute Neural Forward Pass
sample_vector = np.random.randn(16).astype(np.float32)
decision = agent.run_agent_inference(sample_vector)

print("Agentic Action Decision:", decision)

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