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zeekayandHanzo Dev dced1dcfb9 Publish under the org, not a person
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]>
2026-09-01 08:55:40 -07:00

120 lines
4.5 KiB
TeX

\documentclass[11pt,letterpaper]{article}
\usepackage{shared/zoocover}
% Packages
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{amsmath,amssymb,amsthm}
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage{graphicx}
\usepackage{hyperref}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{tikz}
\usepackage{pgfplots}
\pgfplotsset{compat=1.18}
\usetikzlibrary{shapes,arrows,positioning,calc,decorations.pathreplacing}
\usepackage{listings}
\usepackage{caption}
\usepackage{subcaption}
\usepackage{enumitem}
% geometry loaded by zoocover
% Theorem environments
\newtheorem{theorem}{Theorem}[section]
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{proposition}[theorem]{Proposition}
\newtheorem{corollary}[theorem]{Corollary}
\newtheorem{definition}{Definition}[section]
\newtheorem{example}{Example}[section]
% Custom commands
\newcommand{\Zoo}{\textsc{Zoo}}
\newcommand{\Lux}{\textsc{Lux}}
\newcommand{\Beluga}{\textsc{Beluga}}
\newcommand{\BLG}{\textsc{blg}}
\newcommand{\PoAI}{\textsc{PoAI}}
\newcommand{\DAO}{\textsc{dao}}
\newcommand{\LLM}{\textsc{llm}}
\newcommand{\TEE}{\textsc{tee}}
\newcommand{\LoRA}{\textsc{LoRA}}
\newcommand{\QLoRA}{\textsc{QLoRA}}
\newcommand{\GRPO}{\textsc{grpo}}
\newcommand{\DSO}{\textsc{dso}}
% Solidity language definition (listings does not include it by default)
\lstdefinelanguage{Solidity}{
morekeywords={contract,function,returns,return,if,else,for,while,do,
mapping,address,uint256,uint,int,bool,string,bytes,bytes32,
public,private,internal,external,view,pure,payable,memory,storage,
calldata,event,emit,modifier,require,struct,enum,interface,
abstract,override,virtual,is,new,delete,import,pragma,solidity,
library,using,constructor,msg,sender,block,timestamp,this,super},
sensitive=true,
comment=[l]{//},
morecomment=[s]{/*}{*/},
morestring=[b]",
}
% Listings style
\lstset{
basicstyle=\ttfamily\small,
breaklines=true,
frame=single,
numbers=left,
numberstyle=\tiny\color{gray},
tabsize=2,
captionpos=b,
aboveskip=10pt,
belowskip=10pt
}
% Hyperref setup
\hypersetup{
colorlinks=true,
linkcolor=blue,
citecolor=blue,
urlcolor=blue
}
% Title and authors
\title{\textbf{BELUGA}\\
\large L3 App-Chain for AI Model Economics}
\author{
Zoo Labs Foundation Research\thanks{[email protected]}\\
\textit{Zoo Labs Foundation}\\
\texttt{[email protected]}
}
\date{January 2026}
\begin{document}
\zoocoverpage
\begin{abstract}
We present \Beluga{}, a Layer 3 application-specific blockchain for AI model economics, built on \Zoo{} Network (L2) which itself runs on \Lux{} Network (L0). \Beluga{} introduces a dedicated execution environment where every operation---model registration, inference requests, training bounties, data contribution---is a native transaction priced in the \BLG{} gas token. The chain implements custom EVM precompiles for inference pricing (address \texttt{0x0300}), a model marketplace, and training bounty escrow, enabling sub-millisecond settlement of AI compute transactions without leaving the chain. With a fixed supply of 100M \BLG{} allocated across a \DAO{} treasury (60M), inference pool (20M), training bounties (10M), and DEX liquidity (10M), \Beluga{} creates a self-sustaining economy where participants earn by training models, curating datasets, hosting inference, evaluating outputs, and governing the network. The chain runs on existing \Zoo{} validators via the \texttt{zood} node binary, requiring no new infrastructure. We describe the architecture, token economics, earning mechanisms, smart contract design, precompile specification, Proof of AI consensus adaptation, security model, and a concrete migration path from smart contracts on \Zoo{} to a sovereign L3. Experimental results from testnet deployment demonstrate 2,400 inference transactions per second with 1.1-second finality.
\textbf{Keywords}: Layer 3 blockchain, AI model economics, inference marketplace, training bounties, application-specific chain
\end{abstract}
\tableofcontents
\newpage
%% ============================================================
\input{sections/01-introduction}
\input{sections/02-architecture}
\input{sections/03-blg-token-economics}
\input{sections/04-earning-mechanisms}
\input{sections/05-smart-contracts}
\input{sections/06-precompiles}
\input{sections/07-consensus-poai-on-beluga}
\input{sections/08-security-model}
\input{sections/09-migration-path}
\input{sections/10-related-work}
\input{sections/99-conclusion}
\end{document}