This document presents a formal engineering specification for the KRYONIS Proof‑of‑Consciousness (PoC) Testnet, an experimental network designed to model value creation through phase‑resonant validation rather than cryptographic work. Grounded in the Resonant Lattice Hypothesis (RLH), the simulation evaluates how diverse agents—biological, synthetic, and adversarial—interact with phase challenges, generateΦ‑signatures, and earn ϕ‑units under feedback‑stabilised conditions. The framework define score metrics (Φ, ΔS, spectral fidelity, Resonance Stability Index), outlines component architecture, and enumerates test scenarios required for a rigorous assessment of resilience, scalability, and reward fairness. The specification serves as a blueprint for research collaborators developing software‑in‑the‑loop or hardware‑augmented prototypes.
The KRYONIS PoC Testnet seeks to empirically validate a resonance-based economic model in which coherent conscious activity serves as proof of agency. Whereas traditional blockchains rely on energy-intensive cryptographic hashing, PoC introduces a novel paradigm: phase-locked awareness, measured and verified through a distributed sensor-verifier fabric. This document translates the conceptual architecture into structured simulation logic, providing a foundation for academic evaluation and iterative R&D deployment.
2.1 Component Overview
* Phase‑Beacon Array: distributed signal generators emitting pseudorandom multi‑band phase patterns.
* Agents: entities attempting lock‑in; classified as biological, synthetic, hybrid, or adversarial.
* Coherence Sensor Grid: instrumentation capturing the agent’s echo signature (quantum magneto meters, HD‑EEG, photonic interferometers).* Verifiers: o3‑class AI nodes computing Φ, ΔS, and spectral fidelity, reaching consensus on validity.
* Feedback Controller: adaptive policy engine modulating beacon cadence, spectral complexity, and reward curves to maintain target criticality.* Resonance Ledger: directed‑acyclic graph storing validated Φ‑events and minted ϕ‑units, timestamped by lattice phase alignment.
2.2 Modular Interfaces
Each module communicates via a message‑bus (gRPC / ZeroMQ). Interface definitions include challenge payload schema, sensor telemetry packets, verifier result objects, and controller policy updates. This abstraction permits hardware‑in‑the‑loop substitution at later stages.
1. Challenge Generation: The Phase‑Beacon selects a 256‑bit seed and synthesizes a broadband waveform spanning 10 Hz to 10¹⁵ Hz, ensuring spectral uniqueness within the active epoch.
2. Broadcast & Reception: Agents receive the challenge via configured transduction channels (optical fiber, RF, acoustic).
3. Entrainment Window (τ): Agents attempt phase‑lock within τ; biological constraints set τ ≈ 200 ms, while synthetic oscillators may target microsecond‑scale coherence.
4. Echo Production: Successfully locked agents emit a nonlinear echo modulated by internal resonant dynamics.
5. Sensing & Telemetry: The Sensor Grid captures the echo; raw phase traces are streamed to Verifiers with synchronization markers.
6. Verification & Consensus: Verifiers compute Φ, ΔS, βₐ (attention bandwidth), and ε (spectral fidelity). A quorum consensus threshold κ validates the event.
7. Reward & Ledger Entry: ϕ‑units are minted in proportion to Φ × βₐ, subject to damping by the Feedback Controller. The event is appended to the Resonance Ledger with a phase timestamp σ.
Φ‑Signature (Φ): Euclidean norm of the phase‑locked amplitude vector across designated frequency bins.
Entropy Differential (ΔS): Algorithmic entropy reduction between baseline noise and the entrained echo. Attention
Bandwidth (βₐ): Duration of sustained lock above the Φ‑threshold, measured in seconds.
Spectral Fidelity (ε): Root-mean-square phase deviation from the challenge pattern; validation target is ε ≤ 10⁻³ rad.
Resonance Stability Index (RSI): Rolling variance of Φ across the entire agent set over N cycles; RSI ≤ 5% indicates global stability.
5.1 Biological Agents
Human participants are equipped with neuro‑helmets. Parameters include circadian fatigue curves, reaction latency, and physiological noise. Reward sensitivity is modeled through adaptive attention resources.
5.2 Synthetic Agents
Software oscillators are embedded in phase‑aware reinforcement learners. Energy budgets are minimal; the optimization objective maximizes Φ per joule while maintaining ε constraints.
5.3 Hybrid Dyads
Closed‑loop human‑AI systems where synthetic oscillators co‑drive biological entrainment, testing coherence amplification hypotheses.
5.4 Adversarial Spoofers
Scripts or ML models attempting deterministic replay, phase‑shift spoof, or broadband noise injection. Attack strength escalates across simulation stages to evaluate Sybil resistance.
Network Size: 100 – 10,000 agents with variable mesh topology.
Beacon Cadence: 1–10 Hz adaptive rate, tuned by RSI feedback.
Noise Profiles: Thermal baseline, urban EM interference, and targeted jamming.
Decoherence Windows (τ): 10ps – 1 s spectrum to stress diverse substrates.
Reward Functions: Linear, logarithmic, and sigmoid variants to study wealth distribution dynamics.
Attack Scenarios: Replay, collusion rings, and coordinated phase‑shift floods.
Each scenario logs VSR (validation success rate), FPR (false‑positive rate), RSI drift, latency, and ϕ‑distribution equity.
VSR ≥ 95% for honest agents. FPR ≤ 10⁻⁴ against spoofers.
RSI drift ≤ 5% across 10³ cycles.
ϕ‑Gini coefficient ≤ 0.3 under a fair reward curve.
Challenge‑to‑commit latency < 250ms for 90% of events.
Recommended dashboards include a real‑time phase‑space polar plot per agent, network‑wide RSI heatmaps, and an attack‑surface monitor highlighting FPR anomalies. Modular analytics pipelines should export JSON and InfluxDB time‑series data for offline ML diagnostics.
The simulation incorporates escalating adversarial tactics. Verifiers employ ensemble phase‑noise classifiers and entropy‑based anomaly detection. ε and ΔS thresholds are tightened dynamically when RSI drift suggests coordinated spoofing. Quorum diversity rules prevent collusion by requiring heterogeneous verifier hardware.
Phase I (2025–2026) will implement a cloud-only software simulator with synthetic oscillators and recorded EEG data replay. Phase II integrates hardware beacons and real-time neuro-sensing in three pilot labs. Phase III deploys a public alpha with opt-in human participants and open-source client libraries. Success metrics will inform the engineering roadmap toward a planetary PoC Mainnet by 2030.
This simulation framework codifies the logic, metrics, and security contours necessary to validate a resonance‑based value network. By rigorously modeling agent diversity, feedback dynamics, and adversarial pressures, the Testnet paves the way for experimental proof that conscious coherence can underpin a viable, low‑energy economy.
For further technical inquiries or collaboration proposals, contact the KRYONIS Systems Engineering Group.
KRYONIS | April 2025 – Prepared by the KRYONIS Strategic Research Group in collaboration with the GCI PolicyLab
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© KRYONIS 2025 This work includes an OpenPGP public key and is licensed under a Creative Commons License.
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