Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study

2026-06-14 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, FinTech & Digital Financial Services, Banking & Financial Services · Depth: Expert, extended

Summary

The study "Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models" combines two FAOS research efforts. It presents a reduced-power proof-of-mechanism for ontology-amplified distillation, adapting a Qwen3.6-27B student model to a financial ontology. This involved supervised fine-tuning on frontier-teacher trajectories and ontology-grounded direct preference optimization (DPO) using 47 synthetic English preference pairs, trained locally on a single Apple M5 Max. The distilled student grounded 36 of 40 held-out Vietnamese financial-domain tasks (grounded rate 0.90, mean r_onto=0.95), matching the GPT-5 frontier baseline. However, the study notes it is underpowered to establish equivalence, with a 95% confidence interval spanning ±4 tasks, and did not achieve the predicted amplification. Additionally, the paper consolidates a contextuality-audit method for enterprise-agent routing, reporting a negative result where canonical Contextuality-by-Default (CbD) degree was zero, indicating direct influence and construct coupling, not residual contextuality. The findings support neither deployability nor statistical equivalence.

Key takeaway

For AI Architects designing sovereign enterprise LLMs under data-residency rules, this study suggests that ontology-amplified distillation can achieve frontier-level domain grounding with smaller models like Qwen3.6-27B. You should prioritize local, deterministic compliance metrics and consider contextuality audits to differentiate genuine model disagreement from prompt sensitivity before implementing multi-agent synthesis or human review. This approach helps ensure regulatory adherence and efficient workflow routing.

Key insights

Ontology-amplified distillation can align smaller LLMs to domain-specific grounding, matching frontier models on term coverage.

Principles

• Ontology grounding value rises with thinner parametric coverage.
• DPO can isolate and transfer specific behaviors like ontology adherence.
• Contextuality audits distinguish direct influence from residual conflict.

Method

Ontology-amplified distillation involves SFT on frontier-teacher trajectories with injected ontology slices, followed by DPO using ontology-grounded vs. ungrounded answers.

In practice

• Fuse Qwen3.6-27B to a 4-bit checkpoint for local inference.
• Use a local ontology-compliance metric to remove judge-model variance.
• Audit for direct influence before escalating model disagreements.

Topics

• Ontology-Amplified Distillation
• Sovereign LLMs
• Data Residency
• Direct Preference Optimization
• Contextuality Auditing
• Financial Services AI

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.