主权企业语言模型的本体增强蒸馏与上下文审查：机制验证与负面结果的组合方法研究
Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study
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中文总结 AI 辅助
研究针对受数据驻留规则约束的金融机构需求，结合本体增强蒸馏机制验证与上下文审查方法，对Qwen3.6 - 27B学生模型进行训练及测试，结果不支持模型在多方面的优势，为企业语言模型应用提供参考。
中文摘要 AI 辅助
受数据驻留规则约束的受监管金融机构需要可在机构范围内运行的租户自有语言模型。本文将两项相关的FAOS研究整合为一篇关于机制与控制的文章。首先，报告了本体增强蒸馏的低功耗机制验证研究：通过对前沿教师轨迹进行监督微调以及基于本体的直接偏好优化，使Qwen3.6 - 27B学生模型适应Foundation AgenticOS本体，在单个Apple M5 Max上从47个合成的英语跨域偏好对进行本地训练。在40个保留的越南金融领域任务上，蒸馏后的学生模型能完成36个任务（接地率0.90；平均本体术语覆盖率r_onto = 0.95），与GPT - 5前沿基线相同，但结果不足以确立等效性。其次，巩固了企业代理路由的上下文审查方法。在单独的负面结果试点中，本地Qwen运行和明确标记的Gemma复制检查中，所有阶段1.3组的校正规范默认上下文度均为零。这些研究将基于本体的模型构建机制与治理诊断相结合，以决定何时明显的分歧应触发快速标准化、多智能体合成或人工审查。证据不支持可部署性、安全性、优越性、统计等效性或上下文积极路由规则。
英文摘要
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.
发表机构
- AgenticOS（智能操作系统）
机构由 AI 辅助整理，请以论文原文为准。
