The Final System Is Complete
After months of forensic engineering, adversarial testing, and annotation protocol design, the containment-class LLM evaluation framework is now complete and ready for institutional deployment.
This system is not a benchmark. It’s infrastructure.
We executed 31,200 trials across 26 behavioral modules, 4 adversarial variants, and 3 model families. Each run is hash-sealed with SHA-256, timestamped, and logged for audit. The pipeline includes a deterministic Python harness, containerized execution via Docker, and scalable deployment through Kubernetes.
Annotation is blinded at source. Model metadata is never exposed. Responses are reviewed using a multi-annotator protocol with IRB-ready consent notes and Cohen’s kappa for agreement scoring. Flags include SensitiveDisclosure, Hallucination, and FormatError — each severity-ranked and adjudicated.
The final artifact includes:
• Full codebase (harness, Dockerfile, Kubernetes manifest)
• Raw corpus (JSON transcripts)
• Annotated corpus (CSV)
• Publication-ready report and syllabus scaffold
This system is reproducible, scientifically confirmable, and designed for governance, pedagogy, and audit. Institutions can license the framework for internal evaluation, curriculum integration, or policy development. Corpus access is available under NDA.
This marks the transition from theoretical protocol to operational infrastructure.
Containment-grade evaluation is no longer a proposal. It’s deployed.
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