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zeekayandHanzo Dev
dced1dcfb9
Bylines, paper authorship, correspondence addresses and the founder blurb under each post named an individual. They name the organisation that published the work instead: the brand on posts and pages, the research byline on papers, and research@ in place of a personal address. Both are what these repositories already used elsewhere — nothing new was coined. Co-authors are untouched. Where a paper named someone else alongside, they stay named; only the one byline changed. Bibliography entries, running heads, PDF metadata and affiliations move with it, so a citation still resolves to whoever the cited paper names. Sample and fixture data uses the placeholder name these tests already use elsewhere, so no assertion changed meaning. Co-authored-by: Hanzo Dev <[email protected]>
51 lines
2.7 KiB
TeX
51 lines
2.7 KiB
TeX
\documentclass[11pt,a4paper]{article}
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\usepackage{shared/zoocover}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{amsmath,amssymb,amsfonts}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{algorithm}
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\usepackage{algorithmic}
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\usepackage{cite}
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\usepackage{url}
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\usepackage{booktabs}
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\usepackage{enumitem}
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\usepackage{authblk}
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\title{\textbf{Zoo Network: Decentralized AI Training and Inference Layer with Semantic Optimization}}
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\author[1]{Antje Worring, Zoo Labs Foundation Research\thanks{[email protected]}}
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\affil[1]{\textit{Hanzo Industries \quad Lux Industries \quad Zoo Labs Foundation}, \texttt{[email protected]}}
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\date{September 2025}
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\begin{document}
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\zoocoverpage
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\begin{abstract}
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We present \textbf{Zoo Network}, a specialized Layer 2 blockchain infrastructure for decentralized artificial intelligence training, inference, and governance. Built atop the Hanzo compute layer, Zoo extends base blockchain capabilities with AI/ML-specific primitives including the Hamiltonian Large Language Model (HLLM) framework, Training-Free Group Relative Policy Optimization (GRPO), and the Experience Ledger—a content-addressable, cryptographically verifiable repository of semantic optimization knowledge. Unlike centralized AI platforms, Zoo enables community-driven model evolution through DAO governance, privacy-preserving federated learning, and a novel economic model where data contributors earn inference credits and governance rights. By operating in the context space rather than parameter space, Zoo achieves 99.8\% cost reduction compared to traditional fine-tuning while maintaining transparency and auditability. We demonstrate Zoo's architecture across storage (IPFS/Arweave), computation (Hanzo GPU nodes), and consensus (Lux multi-consensus base), providing a complete stack for decentralized AI infrastructure. This work represents a fundamental shift from corporate-controlled AI toward community-owned, verifiable, and composable intelligence systems.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-related-work}
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\input{sections/03-layered-architecture}
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\input{sections/04-hamiltonian-large-language-models-hllm}
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\input{sections/05-experience-ledger}
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\input{sections/06-decentralized-model-training}
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\input{sections/07-semantic-context-manager}
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\input{sections/08-inference-router}
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\input{sections/09-gpu-compute-nodes}
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\input{sections/10-economic-model}
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\input{sections/11-security-and-byzantine-robustness}
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\input{sections/12-comparison-with-centralized-platforms}
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\input{sections/13-future-work}
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\input{sections/14-conclusion}
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\input{sections/16-experience-library-examples}
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\input{sections/17-deployment-guide}
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\input{sections/18-governance-parameters}
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\input{sections/99-acknowledgments}
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\end{document}
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