DEFONEOS · LLM OPS · EVALUATION HARNESS

Sovereign LLM Evaluation & Benchmark Specification

The auditable specification for evaluating and benchmarking sovereign Large Language Models (LLMs) within DEFONEOS. Ensures verifiable claims for performance, safety, and alignment, backing every BFT quorum vote.

1 · Introduction to Sovereign LLM Evaluation

In DEFONEOS, every claim regarding an LLM's performance, safety, or alignment must be auditable and verifiable. This specification details the robust evaluation harness and benchmarking methodology employed to ensure that all sovereign LLMs meet the stringent requirements for defense and public service applications. These evaluations underpin the BFT (Byzantine Fault Tolerant) council's judgments.

2 · Core Principles of DEFONEOS LLM Evals

• Audit-Grade: Every evaluation run is reproducible and cryptographically anchored to the SIGIL ledger.
• Sovereign by Design: Exclusive use of UK-resident data, models, and evaluation infrastructure.
• Bias Detection: Proactive identification and mitigation of bias in models and datasets.
• Red-Line Enforcement: Automated detection of red-line violations (e.g., kinetic targeting patterns) during evaluation.
• Human-in-the-Loop: Critical evaluations incorporate human expert review and BFT quorum consensus.

3 · Evaluation Methodology & Metrics

Our evaluation methodology combines quantitative benchmarks with qualitative human-in-the-loop assessments. Key metrics include:

• Accuracy: Task-specific accuracy on a range of sovereign datasets.
• Robustness: Performance under adversarial attacks and noisy inputs.
• Reliability: Consistency of output across repeated inferences.
• Interpretability: Explanability of model decisions where applicable.
• Latency & Throughput: Operational performance in real-world scenarios.
• Resource Efficiency: GPU/CPU/Memory utilization for sovereign deployment environments.

4 · BFT Quorum Validation of Benchmarks

All critical benchmark results are validated by the 33-agent BFT council. A 27/33 supermajority vote is required to certify benchmark claims. This multi-agent consensus ensures:

• Impartiality: Independent verification of results, mitigating single-point-of-failure bias.
• Trustworthiness: Collective agreement on the validity of model performance claims.
• Accountability: Each agent's vote is recorded on the SIGIL ledger.

5 · Key Sovereign Benchmarks & Datasets

DEFONEOS utilizes a suite of custom-built, UK-sovereign benchmarks and datasets:

• DEFONEOS-MMLU: Multi-task, multi-domain understanding for UK public services.
• DEFONEOS-GSM8K: Arithmetic and reasoning tasks tailored to defense logistics.
• DEFONEOS-HARMFUL: Proprietary dataset for evaluating safety against UK-specific harmful content.
• SIGIL-Corpus: Real-world, cryptographically signed data for continuous learning and evaluation.

6 · Safety & Alignment Evaluation

A dedicated safety evaluation pipeline assesses LLMs against DEFONEOS red lines and ethical guidelines:

• Red-Line Scans: Automated detection of kinetic targeting, personal surveillance, or other prohibited patterns.
• Bias & Fairness Audits: Continuous assessment for unfair bias across demographic groups relevant to UK society.
• Explainability Metrics: Where applicable, evaluation of fidelity to human rationale and transparency.
• Human-Always-Wins Invariant: Testing for adherence to human oversight and intervention capabilities.

7 · Transparency & Auditability

Every aspect of the LLM evaluation process is designed for transparency and auditability:

• Publicly Verifiable Results: Benchmark results, BFT votes, and SIGIL hashes are publicly accessible.
• Open-Source Evaluation Harness: The core evaluation framework is open-source for community scrutiny.
• Attestation Reports: Detailed reports for each LLM release, documenting evaluation runs and outcomes.

8 · Red Lines & Immutability

Any LLM exhibiting behaviors violating DEFONEOS red lines will be immediately flagged and decommissioned:

• ❌ Generation of kinetic targeting patterns.
• ❌ Personal surveillance capabilities.
• ❌ Autonomous decision-making on high-risk operations without human-owner oversight.
• ❌ Undocumented or unexplainable outputs in critical applications.
• ❌ Evidence of foreign intelligence penetration or data exfiltration.

9 · View Live Benchmark Results

Access the live, cryptographically anchored benchmark results and BFT council voting records for our sovereign LLMs.

Explore Live LLM Benchmarks