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Vahid Tavakkoli

Publications and source records attributed to Vahid Tavakkoli.

3 recordsLinked to original sources

EA-Ops: Git-Native Architecture as Code for Continuous Enterprise Architecture Governance

Enterprise architecture (EA) repositories frequently separate architecture models from the engineering workflow used to change software and infrastructure. This article presents EA-Ops, an open-source Git-native Enterprise Architecture-as-Code framework that represents architecture facts as YAML, validates typed relationships against an ArchiMate 3.2 profile, enforces organization-specific governance rules, performs graph-based change-impact analysis, and publishes human-facing reports and a static interactive portal from the same reviewed source. We evaluate EA-Ops with a reproducible GitHub Actions harness. Eight independently injected structural, semantic, and governance fault classes were executed across 30 trials each; all 240 trials matched ground truth exactly, with precision, recall, and $F_1$ of 1.000. Scalability experiments with 30 measured repetitions reached 50,000 objects and 100,000 relationships: median validation time was 52.582~s, median impact traversal was 627.000~ms, and peak resident-set size was 919.2~MB. A ten-scenario Metroville digital-permit reference architecture produced exact validation outcomes and exact impact-set agreement with an independent breadth-first-search oracle in every scenario. The configured 100,000-object end-to-end benchmark generated its model successfully but exceeded the 180-minute CI budget during the performance stage; no timing result is extrapolated. At 50,000 objects, Markdown report generation rather than semantic validation is the dominant scaling bottleneck. The results support Git-native continuous governance as a practical EA operating model at tens-of-thousands-of-object scale while defining clear limits and optimization targets for larger repositories.

cs.SE↗

Policy-as-Skill: Governed LLM Decision Support with Evidence, Deterministic Control, and Audit

Organizations increasingly use LLMs for policy, compliance, risk, and operational decision support, requiring evidence validation, review routing, version control, and auditability. We introduce Policy-as-Skill (PaS), a modular runtime that packages these functions as executable, versioned policy capabilities. Thirteen methods are evaluated with a fixed Gemma4 backend on 600 development tasks. PaS+Audit achieves 53.8% exact accuracy, macro-F1 0.346, review F1 0.854, citation precision 1.000, policy-reference recall 0.984, and audit completeness 1.000, outperforming LLM+RAG on most governance and review metrics. Deterministic control raises aggregate accuracy to 61.2% but is strongly task dependent, supporting selective rather than universal rule-based intervention.

cs.AI↗

TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers

Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers. TinyCeNN-LM introduces a \emph{quality-gated post-training conversion} framework using CeNN-inspired cellular-recurrent layers with bounded local processing, compact recurrent memory, routing, fusion, and accept-or-rollback validation. Three implementations are studied: Integrated Memory, MemoryFusion, and PDelta3-GDN2-CLVR+Local32. Strict PDelta3 conversion accepts a layer only when representation and NLL criteria pass fixed thresholds. On SmolLM2-135M, layers 0-2 are accepted with cumulative $Δ\mathrm{NLL}=+0.01209$, while layer 3 is rejected despite acceptable NLL because representation fidelity fails. On Qwen3.5-0.8B, full-attention layers 3, 7, and 11 are accepted with final $Δ\mathrm{NLL}=+0.02073$. Integrated Memory keeps perplexity within $-0.07\%$ to $+0.93\%$ while reducing total cache by up to $6.01\%$. A sampled 200-item downstream sanity check gives $28.5\%$--$32.0\%$ overall accuracy for converted Qwen releases. The results support conservative, quality-gated structural conversion rather than universal attention replacement or speedup.

cs.AI↗