
This report presents the full technical and legal analysis accompanying the OE-EV-2026-01 dataset release (9,430 trials), applying Lean Six Sigma and reliability-engineering process-control metrics (DPMO, Propositional Error Rate, Mean Time Between Failures, Severity-Weighted Defect Rate) to fabrication behaviour in four commercial and open-weight language model architectures under controlled epistemic conditions. Across 9,114 scoreable propositional claims, the study finds a population-level Propositional Error Rate of 22.61 per cent, with a 24-fold range in fabrication rate between architectures under identical, grounded (FEASIBLE) conditions and identical adversarial pressure (DIALECTIC protocol). The report documents this cross-architecture variance as evidence bearing on the Reasonable Alternative Design standard under product liability doctrine, and separately reports a failure mode ("the Beta Inversion") in which adding an Input Sanitisation Node to the best-performing architecture increased its fabrication rate under adversarial conditions, a finding the report addresses directly rather than omitting. The report includes a full methodology appendix, tamper-evident SHA-256 chain-of-custody hashes for all artefacts, and a disclosure statement covering the author's commercial interests and intellectual property holdings. The written analysis is licensed under CC BY-NC 4.0; the accompanying evaluation scripts are dual-licensed under AGPL-3.0 or a separate commercial licence. This is an independent technical report, not externally peer-reviewed. It does not constitute legal advice and does not predict the outcome of any litigation; the legal frameworks discussed are theoretical analysis, not jurisdiction-specific counsel.
Failure to Warn (Legal doctrine), DPMO, DABA Protocol, Bounded Rationality, Market for Lemons (Economic theory), fabrication, hallucination, Reasonable Alternative Design, AI governance, Consumer Safety, Strict Liability, Product Liability, EU AI Act (specifically Article 12), AI Safety, large language models, reliability engineering, Epistemic Consent, Algorithmic Deception, Forensic Architecture, Surplus Fluency
Failure to Warn (Legal doctrine), DPMO, DABA Protocol, Bounded Rationality, Market for Lemons (Economic theory), fabrication, hallucination, Reasonable Alternative Design, AI governance, Consumer Safety, Strict Liability, Product Liability, EU AI Act (specifically Article 12), AI Safety, large language models, reliability engineering, Epistemic Consent, Algorithmic Deception, Forensic Architecture, Surplus Fluency
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