AI & Data Sovereignty
Sovereign AI Inference: Hierarchical Semantic Architectures as a Prerequisite for Data Sovereignty and Composable Intelligence
This paper argues that practical data sovereignty for AI systems requires architectural separation of contributions, not retroactive unlearning from merged weights. We propose a two-layer sovereignty model built on hierarchical semantic inference architectures: coarse-grained isolation through domain shards, and fine-grained contributor sovereignty through per-contributor adapter modules composed at inference time. Includes formal protocol specifications for contributor registration, ingestion, composition, and verifiable revocation.
LLM Architecture
Hierarchical Semantic Large Language Model Architectures: A DNS-Inspired Approach
A groundbreaking technical paper proposing DNS-inspired hierarchical architectures for scalable LLM deployment. This research demonstrates an 85% reduction in communication overhead and 47% cost savings through intelligent semantic routing and specialized inference engines. The paper presents a novel approach to distributed AI systems that mirrors the proven scalability patterns of internet infrastructure.
AI & Database Systems
From Tables to Vectors: Understanding Large Language Models Through the Lens of Database Evolution
A comprehensive technical guide that demystifies Large Language Models by building on familiar database concepts. This paper bridges the gap between traditional data structures and modern AI architectures, making complex neural network concepts accessible to educated professionals who want to understand how AI actually works at a fundamental level.
