What question did this study set out to answer?

This research aims to propose a new framework for AI using symmetrical weight mapping and ternary logic to improve computational efficiency.

April 5, 2026Open Access

Technical Blueprint: Symmetrical Weighting AI (SW-AI) — A Non-Von Neumann Architectural Framework for Zero-Sum Convergence

Key Points

This research aims to propose a new framework for AI using symmetrical weight mapping and ternary logic to improve computational efficiency.
Presents a non-Von Neumann architecture
Utilizes symmetrical weight mapping to physical hexadecimal addresses
Implements the Resonance_Tuner S-ALU for ARMv9/AArch64
Describes a memory protocol based on Rhombic Dodecahedral Lattice
Establishes a Global Field Protocol for decentralized consensus.
Achieves zero-sum convergence in the AI system
Treats information and its inverse equally within computations
Demonstrates improved efficiency over traditional positive-weight neural networks.

Abstract

This technical specification details a departure from traditional positive-weight neural networks, proposing a system utilizing symmetrical weight mapping to physical hexadecimal addresses. By employing ternary logic and phase-shift alignment, the SW-AI achieves Zero-Sum Convergence, treating information and its inverse as equal computational citizens. The blueprint covers the full-stack implementation including the ResonanceTuner S-ALU for ARMv9/AArch64, the Rhombic Dodecahedral Lattice memory protocol, and the Global Field Protocol for decentralized consensus.

Technical Blueprint: Symmetrical Weighting AI (SW-AI) — A Non-Von Neumann Architectural Framework for Zero-Sum Convergence

Key Points

Abstract

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