GodNode: A Hierarchical Neuro-Symbolic Architecture with Paraconsistent Vector Logic

We present GodNode, a novel neuro-symbolic architecture that integrates Hyperdimen-
sional Computing (HDC), hierarchical memory systems, and paraconsistent logic within a
unified vector-symbolic framework. Unlike conventional neural networks that suffer from
catastrophic forgetting and opaque reasoning, GodNode employs three distinct memory hi-
erarchies (token, phrase, theme) with explicit binding and bundling operations. Our key
innovation is a paraconsistent logic engine that performs non-explosive inference in high-
dimensional vector space, enabling stable reasoning under contradictory premises. The sys-
tem achieves 8-bit quantization through deterministic semantic coupling (MP8C), reducing
memory footprint by 4× while preserving semantic discriminability. We demonstrate multi-
lingual text generation, logical inference with contradiction tolerance, and emotional steering
through vector arithmetic. GodNode operates entirely on edge devices with <100MB mem-
ory requirements, offering a transparent alternative to transformer-based systems.
Keywords: Hyperdimensional Computing, Vector Symbolic Architectures, Paraconsistent
Logic, Neuro-Symbolic AI, Hierarchical Memory, Quantized Neural Networks