ARKHEION AGI 2.0 — Conscious AI System with Quantum-Inspired Processing and Holographic Memory

ARKHEION AGI 2.0 is a comprehensive artificial general intelligence framework comprising 586K LOC Python, 21K LOC C++, and 18K LOC Rust. This release includes 50 academic papers (252 pages) documenting the system's architecture, from quantum-inspired processing and holographic compression to integrated information theory (IIT 4.0) consciousness modeling. Key components: (1) Quantum-inspired processor with AdS/CFT-based compression, (2) HUAM hierarchical memory with holographic encoding, (3) IIT consciousness engine with φ computation, (4) HTCV2 ternary compression achieving 51,929:1, (5) Forge runtime in Rust, (6) Bio-synthetic neural networks on AMD ROCm. All papers include rigorous epistemological notes distinguishing heuristic design metaphors from empirically measured results.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-17
DOI
https://doi.org/10.5281/zenodo.18670266
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00
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article

ARKHEION AGI 2.0 — Conscious AI System with Quantum-Inspired Processing and Holographic Memory

Jhonatan Vieira Feitosa
Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
article

ARKHEION AGI 2.0 — Conscious AI System with Quantum-Inspired Processing and Holographic Memory

Jhonatan Vieira Feitosa
article en

Abstract

ARKHEION AGI 2.0 is a comprehensive artificial general intelligence framework comprising 586K LOC Python, 21K LOC C++, and 18K LOC Rust. This release includes 50 academic papers (252 pages) documenting the system's architecture, from quantum-inspired processing and holographic compression to integrated information theory (IIT 4.0) consciousness modeling. Key components: (1) Quantum-inspired processor with AdS/CFT-based compression, (2) HUAM hierarchical memory with holographic encoding, (3) IIT consciousness engine with φ computation, (4) HTCV2 ternary compression achieving 51,929:1, (5) Forge runtime in Rust, (6) Bio-synthetic neural networks on AMD ROCm. All papers include rigorous epistemological notes distinguishing heuristic design metaphors from empirically measured results.

Zenodo (CERN European Organization for Nuclear Research)
Universidade do Estado do Amazonas (BR), Universidade Federal do Amazonas (BR)
Openalex Percentile: Top 55%
Quantum Computing Algorithms and Architecture
DOIOpen Access PDFOpenAlex
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