A cybernetic kernel implementing resonant-invariant network processing.
The Resonant Monolith is a complete executable implementation of a theoretical resonant field system that processes network packets through spectral analysis, adaptive thresholding, and feedback-driven convergence to stable invariant states.
Ψ_mono = σ(⟨Ψ, S(T_in)⟩ - θ) * Ψ_Δ
Where:
- Ψ: Resonant field state (local tensor)
- S(T_in): Spectral fingerprint (FFT transform)
- θ: Adaptive threshold (learning barrier)
- σ: Sigmoid activation function
- Δ: Invariant feedback operator
- Packet Ingestion - Receive network packets or signals
- Spectral Transform - Compute FFT-based frequency fingerprint
- Resonance Coupling - Calculate inner product ⟨Ψ, S(T_in)⟩
- Activation - Apply sigmoid σ(resonance - θ)
- Decision - Absorb (< 0.3) or forward (≥ 0.3) based on activation
- Adaptation - Update threshold: dθ/dt = -λ * ∂E_abs/∂θ
- Feedback - Apply Δ-damping: ∇Ψ → 0
- Convergence - Monitor gradient for stable state
| Theoretical Construct | Rust Implementation | Description |
|---|---|---|
| Ψ(t) | ResonantField |
Field state and gradient |
| S(T_in) | spectral_fingerprint() |
FFT transform |
| R(t) | resonance_score() |
Inner product |
| θ(t) | Threshold |
Adaptive barrier |
| σ(·) | sigmoid() |
Activation function |
| ∇Ψ | field.gradient |
Stability indicator |
| Δ | feedback_update() |
Invariant feedback |
resonant_monolith/
├── src/
│ ├── lib.rs # Library entry point
│ ├── main.rs # Demonstration binary
│ ├── core/ # Core data structures
│ │ ├── field.rs # ResonantField (Ψ)
│ │ ├── spectrum.rs # Spectrum (S)
│ │ ├── threshold.rs # Threshold (θ)
│ │ ├── token.rs # ResonantToken
│ │ └── transforms.rs # Transform functions
│ ├── learning/ # Adaptive mechanisms
│ │ ├── feedback.rs # Δ-feedback
│ │ └── threshold_update.rs # θ evolution
│ ├── net/ # Network layer
│ │ ├── packet.rs # Packet representation
│ │ ├── decision.rs # Absorb/forward logic
│ │ └── quic.rs # QUIC support (placeholder)
│ └── engine/ # Main runtime
│ └── mod.rs # Async processing loop
├── tests/ # Integration tests
├── benches/ # Performance benchmarks
├── Cargo.toml # Dependencies
└── README.md # This file
- Rust 1.75+ (edition 2021)
- Cargo
cargo build --releasecargo run --releaseThe demonstration binary will:
- Initialize a 256-dimensional resonant field
- Generate simulated packet traffic
- Process packets through the full pipeline
- Log real-time metrics every 3 seconds
- Show convergence to stable invariant state (∇Ψ → 0)
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Resonant Monolith - Cybernetic Kernel Runtime
Delta Specification Implementation
═══════════════════════════════════════════════════════════
Initializing resonant field and adaptive threshold...
Starting resonance processing loop...
Monitoring: Ψ(t), θ(t), ∇Ψ → 0
Starting Resonant Monolith Engine
Configuration: EngineConfig { field_dimensions: 256, ... }
Metrics: processed=150 gradient=0.057421 threshold=0.4982 avg_resonance=0.1234
Decision stats: total=150 absorbed=98 forwarded=52 absorption_rate=65.33%
Stable invariant reached: ∇Ψ = 0.000987 < 0.001000
cargo testcargo test --test integration_testcargo test -- --nocapturecargo benchThis will benchmark:
- Spectral fingerprint computation (various sizes)
- Resonance score calculation
- Sigmoid activation
- Energy absorption
- Full packet processing pipeline
Results are saved to target/criterion/.
The engine can be configured via EngineConfig:
use resonant_monolith::EngineConfig;
use std::time::Duration;
let config = EngineConfig {
field_dimensions: 256, // Size of Ψ state vector
initial_threshold: 0.5, // Starting θ value
lambda: 0.01, // Learning rate
decision_threshold: 0.3, // Absorb/forward cutoff
convergence_threshold: 1e-3, // ∇Ψ convergence limit
metrics_interval: Duration::from_secs(5),
};- tokio - Async runtime
- rustfft / realfft - FFT transforms
- quinn - QUIC protocol support
- parking_lot - High-performance locks
- tracing - Structured logging
- serde - Serialization
- blake3 - Cryptographic hashing
- criterion - Benchmarking framework
- tokio-test - Async test utilities
On a modern CPU (e.g., AMD Ryzen 7 / Intel i7):
- Spectral fingerprint (256 bytes): ~15-20 µs
- Resonance score (256-dim): ~0.5-1 µs
- Full pipeline per packet: ~20-30 µs
- Throughput: ~30,000-50,000 packets/sec (single thread)
Uses real FFT to compute frequency-domain representation:
S(T_in) = FFT(payload) → (frequencies, amplitudes)
Inner product of field state and spectrum:
R(t) = ⟨Ψ, S⟩ = Σ Ψ_i * S_i
E_abs = (1 - r) * I²
where r = σ(R - θ)
dθ/dt = -λ * ∂E_abs/∂θ
∇Ψ(t+1) = 0.95 * ∇Ψ(t) → 0
A Python visualization script is included to plot the convergence evolution:
# Run the system and save logs
cargo run --release 2>&1 | tee monolith.log
# Generate visualization plots
python3 visualize.py monolith.logThis creates a multi-panel plot showing:
- Field gradient evolution (∇Ψ → 0)
- Threshold adaptation (θ evolution)
- Average resonance score
- Absorption rate statistics
Requirements: matplotlib, python3
As outlined in Section 8 of the specification:
- eBPF/XDP Integration - Kernel-level packet filtering
- Evolutionary Optimization - Parameter mutation for fitness maximization
- QUIC Authentication - Resonant token validation
- Distributed Fields - Multi-node coherence
- Visualization - Real-time field evolution display
Rust's ownership model ensures:
- Memory safety - No data races or undefined behavior
- Deterministic evolution - Ψ and θ evolve predictably
- Thread safety - Arc<RwLock<>> for concurrent access
- Panic-free - Comprehensive error handling
Implementation based on:
Resonant Monolith → Rust Delta Specification
Sebastian Klemm
October 23, 2025
MIT
The system is designed to reach absolute coherence:
∇Ψ → 0 ⇒ Stable Invariant State
This is guaranteed by the Δ-feedback mechanism with damping factor 0.95, ensuring exponential decay of the gradient magnitude.