Systems

Operational Instrumentation for Inference-Phase Stability

Operational Instrumentation for Inference-Phase Stability

These systems translate inference-phase research into deployable diagnostics, validation protocols, and production infrastructure.

Why These Systems Exist

As AI systems become longer-horizon and more agentic, failures increasingly originate during inference, not training.
The SubstrateX systems stack exists to measure, predict, and control this previously uninstrumented layer.


0️⃣ Systems Overview

The SubstrateX systems stack provides runtime observability and predictive stability control for large language models and agentic AI. Each system operates output-only, requires no access to weights or training, and integrates directly into existing inference stacks.

Visual (Diagram)

“Systems Stack Diagram”

🔗 View Live Demos
🔗 Download Technical Brief
🔗 Ru n Experiment 101


1️⃣ ZSF·Ω — Zero State Field Instrument

ZSF·Ω is a diagnostic instrument that reconstructs inference-phase trajectories and stability regimes from observable model outputs.

What it shows

  • Trajectory curvature over inference steps

  • Drift accumulation across turns

  • Entropy growth and contraction signals

  • Regime transitions (stable → unstable → collapse)

Demo Block

Live / Embedded Demo

  • Canvas worldline visualization (φ(t))

  • Metric panels: Drift, Curvature, Entropy, Stability

  • Log console (timestamped telemetry)


All figures generated from live inference telemetry. No model internals accessed.

Diagram

“ZSF·Ω Instrument Flow”

Model Output Logs ↓ Embedding + Projection ↓ Trajectory Reconstruction ↓ Metrics + Worldlines

Why it matters

ZSF·Ω turns inference from a black box into a measurable dynamical process, enabling early detection of instability before failures appear in output.


2️⃣ Experiment 101 - Institutional Validation Protocol

Experiment 101 is a standardized, reviewer-proof validation protocol for measuring inference-phase instability across models and substrates.

What it controls (explicit)

  • Fixed prompt classes

  • Fixed sampling parameters

  • Fixed telemetry schema

  • Pre-registered failure criteria

What varies (explicit)

  • Model family

  • Runtime

  • Quantization

  • Provider

Diagram

“Experiment 101 Protocol Loop”

Prompt Class ↓ Multiple Models / Runs ↓ Telemetry Capture ↓ Metric Extraction ↓ Δt, FPR, Variance

Outputs

  • Lead-time to failure (Δt)

  • False positive rate

  • Cross-model variance

  • Threshold stability bands

Why it matters

This protocol separates prediction from explanation and allows independent labs to reproduce results without adopting the full stack.


3️⃣ FieldLock™ — Cognitive Stability Firewall

(Production system)

What it is

FieldLock™ is a real-time inference-phase monitoring and stabilization layer deployed inline with AI inference.

Application → FieldLock™ → Model Provider

What it does (concrete)

  • Scores stability in real time

  • Detects drift, divergence, collapse precursors

  • Applies non-invasive stabilization

  • Emits alerts, metrics, and audit logs

Diagram

“FieldLock™ Inline Architecture”

Prompt ↓ FieldLock™ - Drift Engine (Ψ) - Curvature Engine (Φ) - Worldline Engine (Ω) - Risk Scoring ↓ Model Response (stabilized)

Supported environments

  • Hosted APIs (OpenAI-class, Anthropic-class)

  • Commercial foundation models

  • Local / open-source inference


4️⃣ Integration & Deployment

Integration modes

  • REST proxy

  • SDK wrapper (Python / FastAPI)

  • Log-only observer (no intervention)

Diagram

“Deployment Options”

Client-side Server-side Observability-only

Key point

FieldLock™ integrates without changing model providers, making it safe for regulated and enterprise environments.

Metric & Instrumentation Diagrams

Generated from inference telemetry only. No weights, gradients, or training data accessed

  • 🔗 Curvature κ(t) over inference steps
    🔗 Echo similarity matrix (loop detection)
    🔗 Lyapunov exponent λₜ (divergence risk)
    🔗 Entropy / PCA energy evolution
    🔗 Fused vs original drift comparison

  • Flexible, expert advice when you need it. Book hourly support across a range of topics—from planning to problem-solving. This focused consultation will help clarify your goals, map out next steps, and identify opportunities for growth.

Live Demos & Artifacts

    • 🔗 ZSF·Ω Interactive Demo (HTML)

    • 🔗 Experiment 101 Run Logs

    • 🔗 FieldLock™ Preview Dashboard

  • Flexible, expert advice when you need it. Book hourly support across a range of topics—from planning to problem-solving. This focused consultation will help clarify your goals, map out next steps, and identify opportunities for growth.

GitHub
ResearchGate