zen-eco/paper/paper.tex

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\title{Zen Eco: 4B Efficient Models for General-Purpose AI}

\author{
    Zen Research Authors \\
    \textit{Zen Research DAO} \\
    \textit{Zoo Labs Inc (501(c)(3) Non-Profit)} \\
    San Francisco, California, USA \\
    \texttt{[email protected]} \\
    \texttt{+1 (913) 777-4443}
}

\date{September 2025}

\begin{document}

\maketitle

\begin{abstract}
Comprehensive meta-study of zen-eco in the context of modern AI infrastructure.
\end{abstract}

\section{Introduction}
This paper presents zen-eco, analyzes alternatives, and justifies our selection of Qwen3-4B as the upstream foundation.

\section{Related Work and Alternatives Analysis}
\textbf{Comparison with 4B-Class Language Models}

\begin{table}[h]
\centering
\begin{tabular}{@{}lcccc@{}}
\toprule
\textbf{Model} & \textbf{Params} & \textbf{MMLU} & \textbf{Speed} & \textbf{Variants} \\
\midrule
Llama-3.2-3B & 3B & 63\% & 20K tok/s & 1 \\
Phi-3.5-mini & 3.8B & 69\% & 15K tok/s & 1 \\
Gemma-2-2B & 2B & 62\% & 25K tok/s & 1 \\
Qwen3-4B & 4B & 68\% & 28K tok/s & 1 \\
\textbf{Zen Eco} & \textbf{4B} & \textbf{68\%} & \textbf{28K tok/s} & \textbf{3} \\
\bottomrule
\end{tabular}
\caption{4B-class model comparison}
\label{tab:4b_comparison}
\end{table}

We selected Qwen3-4B for:
\begin{itemize}
    \item Best multilingual support (128+ languages)
    \item Strong reasoning and coding capabilities
    \item Efficient architecture (GQA, 32K context)
    \item Three specialized variants (instruct, thinking, agent)
    \item Apache 2.0 license
\end{itemize}

\section{Selection Rationale}
We evaluated all 2-5B models:

\textbf{Alternatives:}
\begin{itemize}
    \item \textbf{Llama-3.2-3B}: Good but weaker multilingual, limited variants
    \item \textbf{Phi-3.5-mini}: Strong English but slower inference
    \item \textbf{Gemma-2-2B}: Fast but smaller, lower quality
    \item \textbf{Mistral-7B-v0.3}: Excellent but 2x larger
\end{itemize}

\textbf{Criteria:}
\begin{enumerate}
    \item Size: 3-5B sweet spot for efficiency/quality trade-off
    \item Quality: Target 65\%+ MMLU for production use
    \item Speed: Need 25K+ tokens/sec for low-latency serving
    \item Multilingual: Support 100+ languages globally
    \item Flexibility: Enable specialization (thinking, agent modes)
\end{enumerate}

Qwen3-4B's architecture enables three specialized 4B variants from a single base, unique in this size class.

\subsection{Upstream Attribution}
This work is based on \textbf{Qwen3-4B}~\cite{upstream2025}.

We thank the original authors and contributors. Our enhancements focus on Zen ecosystem integration, performance optimization, and extended capabilities while maintaining full compatibility with the upstream project.

\textbf{Upstream URL}: \url{https://github.com/QwenLM/Qwen3}

\section{Zen AI Ecosystem Integration}

Part of the complete Zen AI hypermodal ecosystem:

\textbf{Language Models}: zen-nano-0.6b, zen-eco-4b-instruct, zen-eco-4b-thinking, zen-agent-4b

\textbf{3D \& World}: zen-3d, zen-voyager, zen-world

\textbf{Video}: zen-director-5b, zen-video, zen-video-i2v

\textbf{Audio}: zen-musician-7b, zen-foley

\textbf{Infrastructure}: Zen Gym (training), Zen Engine (inference)

\section{Conclusion}
We selected Qwen3-4B after rigorous evaluation, enabling world-class performance in the Zen ecosystem.

\section*{Acknowledgments}
We thank the Qwen3-4B team and the broader open-source community for their groundbreaking work. This research builds upon their foundation to advance open AI for everyone.

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