{"slug": "ontology-amplified-distillation-and-contextuality-auditing-for-sovereign-models", "title": "Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study", "summary": "A proof-of-mechanism study found that a Qwen3.6-27B student model distilled via ontology-amplified techniques matched GPT-5 on grounding Vietnamese financial tasks, but the result was underpowered to establish equivalence. A separate negative-results pilot showed zero contextuality in enterprise-agent routing, undermining a proposed governance diagnostic. The combined studies do not support deployability, safety, or superiority claims.", "body_md": "arXiv:2607.11948v1 Announce Type: new\nAbstract: Regulated financial institutions operating under data-residency rules need tenant-owned language models that can run inside the institution's perimeter. This paper combines two related FAOS studies into one mechanism-and-control article. First, it reports a reduced-power proof-of-mechanism study of ontology-amplified distillation: a Qwen3.6-27B student is adapted to the Foundation AgenticOS ontology through supervised fine-tuning on frontier-teacher trajectories and ontology-grounded direct preference optimization (DPO), trained locally on a single Apple M5 Max from 47 synthetic, English-language, cross-domain preference pairs. On 40 held-out Vietnamese financial-domain tasks, the distilled student grounds 36 of 40 tasks (grounded rate 0.90; mean ontology term-coverage r_onto = 0.95 on a metric floored at 0.50), equal to the GPT-5 frontier baseline, which also grounds 36 of 40. The outcome is underpowered to establish equivalence: the paired-difference 95% confidence interval spans +/-4 tasks, and the run does not test or show the pre-registered amplification prediction that the student should exceed the frontier. Second, the paper consolidates a contextuality-audit method for enterprise-agent routing. In a separate negative-results pilot, the corrected canonical Contextuality-by-Default degree is zero for all Phase 1.3 groups in both the local-Qwen run and an explicitly labeled Gemma replication check; the useful signal is direct influence and construct coupling, not surviving residual contextuality. Together, the studies pair an ontology-grounded model-building mechanism with a governance diagnostic for deciding when apparent disagreement should trigger prompt standardization, multi-agent synthesis, or human review. The evidence supports neither deployability, safety, superiority, statistical equivalence, nor a contextuality-positive routing rule.", "url": "https://wpnews.pro/news/ontology-amplified-distillation-and-contextuality-auditing-for-sovereign-models", "canonical_source": "https://arxiv.org/abs/2607.11948", "published_at": "2026-07-15 04:00:00+00:00", "updated_at": "2026-07-15 04:22:09.183910+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety", "ai-ethics"], "entities": ["Qwen", "Foundation AgenticOS", "Apple M5 Max", "GPT-5"], "alternates": {"html": "https://wpnews.pro/news/ontology-amplified-distillation-and-contextuality-auditing-for-sovereign-models", "markdown": "https://wpnews.pro/news/ontology-amplified-distillation-and-contextuality-auditing-for-sovereign-models.md", "text": "https://wpnews.pro/news/ontology-amplified-distillation-and-contextuality-auditing-for-sovereign-models.txt", "jsonld": "https://wpnews.pro/news/ontology-amplified-distillation-and-contextuality-auditing-for-sovereign-models.jsonld"}}