Standard // GB-Benchmark-01: Fiduciary Unit Economics for AI

[GB-Benchmark-01] Technical Specification

This document defines the Evidentiary Standard for the Glass Box Architecture. It provides the audited methodology and unit economics behind the performance metrics cited in the Glass Box Manifesto.

1. Methodology: The Fiduciary Audit

Performance in a Glass Box environment is measured by Deterministic Reliability rather than probabilistic throughput. The GB-Benchmark-01 standard requires three points of proof:

1. Perimeter Enforcement (L0): Filtering of anomalous inference requests before they reach the model.
2. Forensic Velocity (L6): The time required to achieve absolute root-cause identification for an inference failure.
3. Token Efficiency (L2): The elimination of unmetered context waste through deterministic steering.

2. Benchmark A: Perimeter ROI (L0)

The 21% GPU Savings metric is derived from the rejection of sub-threshold anomalous requests.

Implementation Note: The L0 Rust Shim (GB-001) intercepts requests at the edge. By rejecting requests that fail the Benford distribution check, we eliminate "shadow compute" costs that typically account for 15-28% of enterprise inference bills.

3. Benchmark B: Forensic Resolution (L6)

The 4-Minute RCA metric defines the transition from manual "archaeology" to deterministic replay.

• Status Quo (Black Box): 4-week average lead time for root-cause identification.
• Target State (Glass Box): 4-minute average lead time for root-cause replay.
• Methodology: Layer 6 (Causal Trace) archives the exact seed, temperature, and context anchors of every inference, allowing for instantaneous exact-match replay.

4. Benchmark C: Resource Efficiency (L2)

The 40% Reduction in Token Waste is achieved through deterministic context steering.

• Context Steering (L2): Prevents "Hallucinated Context" by pinning RAG fragments to deterministic schema anchors.
• Result: 40% reduction in unnecessary token consumption per inference lifecycle.

5. Audit Compliance

To meet the [GB-Benchmark-01] standard, a system must demonstrate: - Immutable Logging: Every L0 rejection must be cryptographically anchored. - Deterministic Replay: Any outlier inference must be reproducible within a 0% token variance. - Transparency: Live reporting of current Anomaly Rejection Rates (ARR).

Download the Full Audit Framework (PDF) →