Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study
This study combines ontology-amplified distillation and contextuality auditing for building and governing tenant-owned language models in regulated financial institutions. The distillation experiment shows a Qwen3.6-27B student grounds 36/40 Vietnamese financial tasks, matching GPT-5, but is underpowered to establish equivalence. A contextuality audit pilot finds zero residual contextuality, suggesting direct influence and construct coupling are more useful signals. The evidence does not support deployability, safety, or superiority.
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[Submitted on 11 Jul 2026]
Title:Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study
View a PDF of the paper titled Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study, by Thanh Luong Tuan
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Abstract: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.
Comments: 15 pages, 2 figures. Combined proof-of-mechanism and negative-results method article consolidating ontology-amplified distillation with contextuality-audit routing for enterprise agents
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2607.11948 [cs.AI]
(or arXiv:2607.11948v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.11948
arXiv-issued DOI via DataCite
Submission history
From: Thanh Luong Tuan [view email] [v1] Sat, 11 Jul 2026 15:42:40 UTC (107 KB)
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