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Scaling Semantic Resilience: A Case Study in 200-Node Global Knowledge Distribution and AI-Benchmarking

Authors: Dreshmanis, Maris;

Scaling Semantic Resilience: A Case Study in 200-Node Global Knowledge Distribution and AI-Benchmarking

Abstract

The digital divide is often a linguistic one. While AI development accelerates, its documentation and evaluation remain siloed in high-resource languages. This paper details the deployment of Reincarnatiopedia, a distributed knowledge network utilizing 202 subdomains. We demonstrate that by combining a 'Less Than Free' service strategy with Native-First LLM generation, it is possible to maintain high E-E-A-T across a massive, multi-regional infrastructure. The paper covers hreflang matrix optimization for 200+ nodes, edge orchestration with Cloudflare Workers, and contextual synthesis using DeepSeek and Gemini for semantic uniqueness across languages. Version 2.0 — revised per Diamond Standard (30-block academic structure). Reviewed by multi-model AI Consilium.

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