R-AIO Data Physics in Edge AI Architecture - Part XII: The Hydrodynamic Paradox of Edge AI: Venturi Effect in SLM Quantization and Neuro-Symbolic Ejector Architectures.
Year: 2026
Type: article
Abstract: Title: The Hydrodynamic Paradox of Edge AI: A Neuro-Symbolic L0 Architecture for Critical Infrastructure Abstract: Deploying probabilistic, cloud-based artificial intelligence models (LLMs) in critical infrastructure systems (OT, e.g., mCDU liquid cooling loops, tactical DC microgrids) faces fundamental barriers: unacceptable latency, exceeding strict power budgets (often < 5W), and a lack of mathematical determinism in execution layers ("hallucinations"). This publication introduces the concept of Data Physics and presents the Sovereign L0 Edge Oracle (v3.1 SIL-Ready)—a unified neuro-symbolic architecture that solves this problem through a direct isomorphism of fluid mechanics principles at the codebase level. Utilizing the computational equivalent of the "Hydrodynamic Paradox," the system processes nonlinear semantic chaos into a hard, deterministic control vector. The architecture is divided into two coupled stages: Venturi Constriction (Probabilistic Layer): The application of a quantized small language model (4-bit SLM) combined with a strict GBNF grammar mask. From the perspective of information theory, the forced reduction of the token space acts as a hydrodynamic restrictor—it deliberately annihilates semantic noise, drastically reduces VRAM pressure, and maximizes the speed of intent extraction (throughput time < 300 ms). Ejector Suction (Deterministic Layer): The highly accelerated, reduced data vector is drawn directly into a statically pre-allocated formal verification SMT engine (Z3-Solver). The solver utilizes Linear Real Arithmetic (LRA) approximation to verify the physical laws of the target domain (e.g., cavitation curves, pressure drops) in hard real-time (response < 10 ms), establishing an impenetrable safety guardrail against AI-induced errors. Version 3.1 introduces an architecture ready for SIL-2/3 rigor, implementing hardware separation of intent from measurement (Sensor Fusion Guard), time-to-live (TTL) telemetry validation, memory management in the critical section (GC locks), and IEEE 754 floating-point safety boundaries. This work proves the feasibility of integrating hybrid AI systems with classical PLC/OT control loops without relying on cloud computing. Concurrently, the provided code and technical documentation constitute a formal defensive publication (establishing Prior Art, ref. UPRP P.454742), legally preventing the commercial appropriation of this design pattern. Keywords: Edge AI, Neuro-Symbolic AI, Z3-Solver, Contested Logistics, Data Physics, SMT, L0 Critical Infrastructure, Defensive Publishing, Prior Art.
Author: Olszewski, Rajmund
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223326922