Let's Make a Token-Using for AI to Cheap

5281/zenodo.19712852 Let’s Make Token-Based This work proposes a radically different approach to artificial intelligence — one that reduces dependence on expensive data-center infrastructure and rethinks how meaning is processed. Instead of relying on large-scale token-based computation, the paper introduces a hybrid framework combining: Holographic matrices (multidimensional data encoding through interference patterns) Pipeline processing architectures (modular, parallel, and incremental computation) Probabilistic decision fields (context-aware, resource-efficient reasoning) The result is a system capable of real-time analysis, adaptive reasoning, and high-speed pattern recognition — even under limited computational resources. 🔹 Core Idea Modern AI systems simulate understanding through massive computation.This work proposes the opposite: Do not simulate meaning — detect it through coherence. Data is treated not as tokens, but as multidimensional fields, where relationships emerge via correlation, interference, and weighted aggregation. 🔹 Key Contributions Introduction of holographic matrices as compact, multidimensional data structures Development of pipeline-based AI processing, enabling: parallel computation incremental insight generation real-time decision support Integration of human intuition as a system component, allowing: adaptive parameter tuning contextual interpretation dynamic creation of analytical masks Demonstration of low-cost optical implementations, using: lasers diffraction gratings spatial light modulators 🔹 Why It Matters This framework challenges a fundamental assumption in AI: Intelligence does not require exponential compute — it requires the right structure. The proposed approach enables: orders-of-magnitude reduction in cost energy-efficient computation scalable edge intelligence systems real-time OSINT and decision analysis 🔹 Applications Open-Source Intelligence (OSINT) Healthcare diagnostics Financial analytics and trading systems Autonomous vehicles and robotics Smart cities and environmental monitoring Industrial automation 🔹 Conceptual Position The system is not purely machine-driven. It is designed as a symbiotic intelligence architecture, where: machines provide speed, scale, and pattern detection humans provide intuition, context, and meaning 🔹 Key Insight The future of AI is not bigger models.It is coherent systems that combine physics, computation, and human intuition. 🔹 Keywords holographic computing, pipeline processing, multidimensional data, OSINT, low-cost AI, edge intelligence, human-AI symbiosis, probabilistic decision fields, optical computing, coherence-based AI AI Cheap: Multidimensional Data Processing Through Holographic Matrices and Pipeline Architectures