ZPE-1: Zero-Point Evolution Engine — Deterministic Adaptive Drift Simulation Framework for Stress Evolution Modeling. Technical Source Document, v1.0.

ZPE-1 Technical Source Document

Zero-Point Evolution Engine (ZPE-1)

Deterministic Adaptive Drift Simulation Framework for Stress Evolution Modeling

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Abstract

ZPE-1 (Zero-Point Evolution Engine) is a deterministic adaptive simulation framework designed to model stress evolution, entropy accumulation, and cascade propagation in high-density multi-node systems. It operates as an offline drift modeling and forecasting engine capable of generating predictive stress signatures under bounded entropy constraints. ZPE-1 does not enforce runtime safety but provides calibrated scenario generation, cascade amplification modeling, and stress-response forecasting for infrastructure-scale control systems.

The engine emphasizes deterministic numeric reproducibility, ledger-auditable simulation states, and quantized drift evolution to ensure cross-platform consistency.

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1. Purpose and Scope

ZPE-1 is designed to:

1. Model non-linear stress evolution across distributed nodes.

2. Simulate entropy production and dissipation dynamics.

3. Generate predictive cascade (echo-risk) signatures.

4. Produce near-miss datasets for control calibration.

5. Stress-test barrier margins under adversarial disturbances.

ZPE-1 is explicitly not:

• A runtime safety controller

• A hardware enforcement layer

• A real-time actuator control module

• A replacement for CBF/MPC safety guarantees

It is a simulation and forecasting environment.

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2. Core Mathematical Model

2.1 Drift Evolution Operator

The core state update operator is:

[
\Omega = (state + bias) \times \alpha
]

Where:

• state: current stress vector or scalar

• bias: structural asymmetry or disturbance offset

• ( \alpha ): bounded amplification scalar

This operator enables modeling of stress amplification and directional drift under controlled bounds.

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2.2 Drift Continuity Rule

State evolution follows:

[
\Omega_{n+1} = \Psi_\ell(\Omega_n)
]

Where ( \Psi_\ell ) represents a bounded drift transform incorporating:

• Temporal delta

• Coherence constraint

• Flexibility factor

Drift evolution is constrained to prevent uncontrolled divergence during simulation.

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2.3 Entropy Modeling

Entropy is computed using:

• Fixed window size (recommended 32)

• Integer-domain histogram

• Fixed-point lookup tables

• No transcendental runtime dependency

This ensures deterministic simulation reproducibility.

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3. Deterministic Numeric Architecture

ZPE-1 enforces strict numerical discipline:

• IEEE 754 float64 (if floating path used)

• FMA disabled

• Round-to-nearest-even

• No fast-math optimizations

• Fixed quantization prior to serialization (recommended 1e-12)

All simulation states may be ledgered using:

• UTF-8 canonical JSON

• Lexicographically sorted keys

• 17 significant decimal digits

• SHA-256 hashing

This allows simulation outputs to be reproducible across architectures.

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4. Multi-Agent Stress Lattice

ZPE-1 supports distributed stress modeling across a lattice of logical nodes (recommended N = 43 for full-scale modeling).

Node roles may include:

• Drift Prediction

• Thermal Modeling

• Latency Modeling

• Amplification Detection

• Entropy Regulation

• Stability Analysis

The lattice architecture allows:

• Cross-node resonance modeling

• Echo-risk amplification detection

• Cascade propagation mapping

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5. Echo-Risk Detection

Echo-risk is defined as a predictive stress amplification pattern where:

[
\Omega_{predicted} > \Omega_{threshold}
]

Even when immediate stress index ( \chi < 1 ).

ZPE-1 can identify:

• Latent cascade vectors

• Non-local stress reinforcement

• Cross-sector resonance patterns

• Phase-aligned amplification events

This allows pre-threshold intervention modeling.

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6. Use in Choke Prevention Systems

When integrated with deterministic choke control architectures:

ZPE-1 provides:

• Synthetic near-miss generation

• Entropy weight estimation

• Dissipation margin stress testing

• Barrier robustness validation

• Adversarial stress injection modeling

ZPE-1 does not participate in runtime control enforcement.

All runtime safety guarantees remain:

• Control Barrier Function certified

• Hardware-enforced (e.g., UCC)

• Deterministic fixed-point constrained

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7. Calibration Role

ZPE-1 may be used during deployment to:

1. Simulate worst-case stress cascades.

2. Tune entropy weights ( a_\cdot ).

3. Estimate safe barrier margins ( \rho ).

4. Validate quantization-induced drift bounds.

5. Generate logistic regression datasets for near-miss modeling.

All calibrated constants must be frozen and ledgered before production activation.

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8. Determinism and Ledger Compatibility

ZPE-1 aligns with deterministic infrastructure requirements by:

• Eliminating floating-point nondeterminism where required

• Supporting integer-domain entropy paths

• Enforcing canonical serialization

• Producing reproducible SHA-256 state hashes

Simulation logs can therefore be:

• Audited

• Replayed

• Verified cross-architecture

• Included in deterministic consensus systems

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9. System Separation Principle

For safety-critical deployments:

• ZPE-1 must remain offline.

• ZPE-1 outputs must not bypass runtime safety shields.

• Hardware invariance enforcement must remain independent.

• Simulation outputs must pass through deterministic control gates.

This separation preserves certification integrity.

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10. Conclusion

ZPE-1 is a deterministic adaptive drift simulation framework designed to model stress evolution, entropy accumulation, and cascade propagation across distributed infrastructure systems.

It provides:

• Predictive echo-risk modeling

• Multi-node cascade simulation

• Deterministic numeric reproducibility

• Ledger-compatible state evolution

When paired with hardware-enforced choke prevention architectures, ZPE-1 strengthens calibration and forecasting without compromising runtime safety guarantees.

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