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

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Auteur principal: Kurumalla, Venkataramana
Format: Recurso digital
Publié: Zenodo 2026
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author Kurumalla, Venkataramana
author_facet Kurumalla, Venkataramana
contents <p>We present GodNode, a novel neuro-symbolic architecture that integrates Hyperdimen-<br>sional Computing (HDC), hierarchical memory systems, and paraconsistent logic within a<br>unified vector-symbolic framework. Unlike conventional neural networks that suffer from<br>catastrophic forgetting and opaque reasoning, GodNode employs three distinct memory hi-<br>erarchies (token, phrase, theme) with explicit binding and bundling operations. Our key<br>innovation is a paraconsistent logic engine that performs non-explosive inference in high-<br>dimensional vector space, enabling stable reasoning under contradictory premises. The sys-<br>tem achieves 8-bit quantization through deterministic semantic coupling (MP8C), reducing<br>memory footprint by 4× while preserving semantic discriminability. We demonstrate multi-<br>lingual text generation, logical inference with contradiction tolerance, and emotional steering<br>through vector arithmetic. GodNode operates entirely on edge devices with <100MB mem-<br>ory requirements, offering a transparent alternative to transformer-based systems.<br>Keywords: Hyperdimensional Computing, Vector Symbolic Architectures, Paraconsistent<br>Logic, Neuro-Symbolic AI, Hierarchical Memory, Quantized Neural Networks</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_18497780
institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle GodNode: A Hierarchical Neuro-Symbolic Architecture with Paraconsistent Vector Logic
Kurumalla, Venkataramana
<p>We present GodNode, a novel neuro-symbolic architecture that integrates Hyperdimen-<br>sional Computing (HDC), hierarchical memory systems, and paraconsistent logic within a<br>unified vector-symbolic framework. Unlike conventional neural networks that suffer from<br>catastrophic forgetting and opaque reasoning, GodNode employs three distinct memory hi-<br>erarchies (token, phrase, theme) with explicit binding and bundling operations. Our key<br>innovation is a paraconsistent logic engine that performs non-explosive inference in high-<br>dimensional vector space, enabling stable reasoning under contradictory premises. The sys-<br>tem achieves 8-bit quantization through deterministic semantic coupling (MP8C), reducing<br>memory footprint by 4× while preserving semantic discriminability. We demonstrate multi-<br>lingual text generation, logical inference with contradiction tolerance, and emotional steering<br>through vector arithmetic. GodNode operates entirely on edge devices with <100MB mem-<br>ory requirements, offering a transparent alternative to transformer-based systems.<br>Keywords: Hyperdimensional Computing, Vector Symbolic Architectures, Paraconsistent<br>Logic, Neuro-Symbolic AI, Hierarchical Memory, Quantized Neural Networks</p>
title GodNode: A Hierarchical Neuro-Symbolic Architecture with Paraconsistent Vector Logic
url https://doi.org/10.5281/zenodo.18497780