papers/zen/zen3-vl_whitepaper.tex

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\title{\textbf{Zen3-VL: Third-Generation Flagship Vision-Language Model}\\
\large Technical Whitepaper v2025.06}
\author{Antje Worring, Hanzo AI Research, Zen LM Research Team\\
\texttt{[email protected]}\\
\href{https://papers.zenlm.org}{papers.zenlm.org}}
\date{June 2025}

\begin{document}
\maketitle

\begin{abstract}
Zen3-VL is our flagship third-generation vision-language model at 72B parameters,
delivering frontier-level visual understanding across document analysis, scientific
reasoning, and complex visual question answering, with particular strength in structured
data interpretation and fine-grained image understanding. Built on the Zen MoDE
(Mixture of Distilled Experts) architecture with a dedicated 6B-parameter high-resolution
vision encoder, Zen3-VL achieves MMMU 72.1\%, MMBench 91.3\%, DocVQA 94.8\%,
ChartQA 89.2\%, and TextVQA 85.4\% --- establishing new state-of-the-art results
across all five primary vision-language benchmarks simultaneously. The model features
dynamic ultra-high-resolution input (up to 4K), scientific figure parsing with
structured output extraction, and a specialized chart-to-data pipeline that converts
visual data representations into structured tables with high fidelity.
\end{abstract}

\tableofcontents
\newpage

%% -----------------------------------------------------------------------
\section{Introduction}
\label{sec:intro}

Vision-language models face a fundamental tension: general visual understanding requires
broad training across diverse image types, yet specialized capabilities --- reading
dense documents, parsing scientific figures, interpreting charts --- demand domain-specific
architectural components and training data. Prior approaches either sacrifice specialization
for generality or maintain separate model checkpoints for different visual domains.

Zen3-VL resolves this tension through a unified 72B parameter model with a shared
Zen MoDE backbone that routes visual tokens through specialized expert clusters
without requiring separate models per task. The key design choices are:

\begin{enumerate}
\item \textbf{High-resolution dynamic tiling} --- Input images up to 4096$\times$4096
      pixels are processed via adaptive tile decomposition, preserving fine-grained
      detail in dense text and chart elements without fixed resolution downsampling.
\item \textbf{Document-specialized vision encoder} --- A 6B ViT-H encoder pre-trained
      on 2B document images provides representations tuned for text layout, table
      structure, and mathematical notation.
\item \textbf{Scientific figure parser} --- A dedicated parsing head extracts
      structured data (axis labels, data series, legends) from scientific figures,
      enabling accurate chart-to-table conversion.
\item \textbf{72B Zen MoDE backbone} --- The language backbone uses 128 experts with
      top-6 routing, providing 660B effective parameter capacity for complex
      multi-step visual reasoning.
\end{enumerate}

%% -----------------------------------------------------------------------
\section{Architecture}
\label{sec:arch}

\subsection{High-Resolution Vision Encoder}

The vision encoder is a ViT-H variant with 6.5B parameters, trained from scratch
on a document-rich image corpus. Key architectural choices:

\begin{itemize}
\item 16$\times$16 pixel patch size (finer than standard 32$\times$32)
\item 32 transformer layers, 48 attention heads, 2048-dimensional hidden state
\item Flash Attention 3 for memory-efficient processing of long patch sequences
\item Learnable class tokens for global image summary alongside local patch tokens
\end{itemize}

For a 4096$\times$4096 input image, the encoder produces $256 \times 256 = 65{,}536$
patch tokens. These are compressed to a manageable sequence via a hierarchical
cross-attention pooling layer that produces 2048 summarized visual tokens while
preserving local detail via a complementary sparse attention bypass.

\subsection{Dynamic Resolution Tiling}

Input images are adaptively tiled based on aspect ratio and content complexity:

\begin{equation}
N_{\text{tiles}} = \left\lceil \frac{W}{W_{\text{base}}} \right\rceil \times \left\lceil \frac{H}{H_{\text{base}}} \right\rceil, \quad W_{\text{base}} = H_{\text{base}} = 448
\end{equation}

Each tile is processed independently by the vision encoder and the resulting tile
tokens are arranged in a 2D grid with position embeddings encoding tile row, column,
and intra-tile patch position. A cross-tile attention layer fuses information across
tile boundaries, critical for reading text that spans tile boundaries.

Maximum supported configurations:
\begin{itemize}
\item Standard: 1--4 tiles (up to 896$\times$896 effective resolution)
\item High-resolution: 4--16 tiles (up to 1792$\times$1792)
\item Ultra-high-resolution: 16--64 tiles (up to 3584$\times$3584)
\item Document mode: up to 128 tiles for A0-format poster parsing
\end{itemize}

\subsection{Zen MoDE Backbone at 72B}

The language backbone uses a 72B Zen MoDE configuration:

\begin{itemize}
\item 80 transformer layers, 8192-dimensional hidden state
\item 64 query heads, 8 key-value heads (GQA ratio 8:1)
\item 128 experts, top-6 active per token (660B total parameter capacity)
\item 131,072 token context window (native, no interpolation required)
\item RoPE with base frequency $\theta = 1{,}000{,}000$ for long-context stability
\end{itemize}

The MoDE routing for VL tasks shows strong specialization: experts 1--24 handle
primarily text tokens, experts 25--64 handle image-text alignment, experts 65--96
specialize in structured data reasoning (tables, charts), and experts 97--128
handle spatial reasoning and fine-grained visual question answering.

\subsection{Scientific Figure Parser}

A dedicated parsing head sits alongside the main language modeling head:

\begin{equation}
\hat{D} = f_{\text{parse}}(\mathbf{v}_{\text{img}}, \mathbf{h}_{\text{backbone}})
\end{equation}

where $\mathbf{v}_{\text{img}}$ is the pooled vision representation and
$\mathbf{h}_{\text{backbone}}$ is the last-layer hidden state. The parser
outputs a structured JSON document conforming to a schema covering:
bar charts, line charts, scatter plots, pie charts, heatmaps, and box plots.

This structured output is then optionally rendered back to a human-readable
description or a Markdown table by the main language head, enabling downstream
programmatic processing.

%% -----------------------------------------------------------------------
\section{Training}
\label{sec:training}

\subsection{Vision Encoder Pre-Training}

The vision encoder is pre-trained in two phases:

\textbf{Phase 1 --- Contrastive pre-training}: CLIP-style contrastive training on
2B image-text pairs from web data, academic papers, and document repositories.
Batch size 65,536, InfoNCE loss with temperature 0.07.

\textbf{Phase 2 --- Document-domain fine-tuning}: Masked image modeling on 500M
document images (PDFs, scanned books, charts, scientific figures) using a DINO-v2
objective. This instills strong layout and text-aware representations.

\subsection{Backbone Pre-Training}

The Zen MoDE 72B backbone is pre-trained on 15T tokens of text before vision
integration. This includes the full Zen family base pre-training corpus plus:

\begin{itemize}
\item Scientific papers: 180B tokens from arXiv, Semantic Scholar, PubMed
\item Technical documents: 120B tokens from patents, standards, manuals
\item Structured data: 80B tokens from tables, spreadsheets, databases
\end{itemize}

\subsection{Multimodal Training}

Vision-language integration training proceeds in three stages:

\textbf{Stage 1 --- Connector training}: Train only the vision-to-language projection
layer (2-layer MLP) with frozen encoder and backbone. Data: 500M image-text pairs.

\textbf{Stage 2 --- Full VL pre-training}: Unfreeze all components and train jointly
on 4B multimodal samples including document understanding, chart QA, scientific
figure understanding, and general VQA.

\textbf{Stage 3 --- Instruction tuning}: Fine-tune on 200M multimodal instruction
samples with chain-of-thought reasoning traces. Includes:
\begin{itemize}
\item Document question answering with page-level context
\item Chart and table extraction with structured output
\item Scientific figure interpretation with mathematical notation
\item Multi-image reasoning requiring cross-image comparison
\end{itemize}

\subsection{Chart-to-Data Synthetic Data Pipeline}

A significant innovation is a synthetic data pipeline that generates 100M training
samples from known chart data:

\begin{enumerate}
\item Sample a data table from a structured database (financial, scientific, census)
\item Render the table as a chart using matplotlib/plotly with randomized styling
\item Generate question-answer pairs about the chart's content
\item Train the model to reconstruct the original data table from the chart image
\end{enumerate}

This creates a closed-loop training signal where ground truth is always available,
enabling precise supervision for chart parsing tasks.

%% -----------------------------------------------------------------------
\section{Evaluation}
\label{sec:eval}

\subsection{General Vision-Language}

\begin{table}[H]
\centering
\caption{General vision-language benchmarks}
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{Params} & \textbf{MMMU} & \textbf{MMBench} & \textbf{SeedBench} & \textbf{MathVista} \\
\midrule
Zen-VL      & 72B & 62.3 & 85.4 & 77.8 & 58.1 \\
Zen2-VL     & 72B & 67.8 & 88.9 & 82.1 & 65.4 \\
\textbf{Zen3-VL} & \textbf{72B} & \textbf{72.1} & \textbf{91.3} & \textbf{86.7} & \textbf{71.8} \\
\bottomrule
\end{tabular}
\label{tab:general_vl}
\end{table}

\subsection{Document and OCR Understanding}

\begin{table}[H]
\centering
\caption{Document understanding and OCR benchmarks}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{DocVQA} & \textbf{TextVQA} & \textbf{OCRBench} & \textbf{InfoVQA} \\
\midrule
Zen-VL      & 89.3 & 79.1 & 75.3 & 71.4 \\
Zen2-VL     & 92.1 & 82.8 & 79.6 & 76.2 \\
\textbf{Zen3-VL} & \textbf{94.8} & \textbf{85.4} & \textbf{82.1} & \textbf{80.7} \\
\bottomrule
\end{tabular}
\label{tab:doc}
\end{table}

\subsection{Chart and Structured Data}

\begin{table}[H]
\centering
\caption{Chart and structured data understanding benchmarks}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{ChartQA} & \textbf{TableVQA} & \textbf{PlotQA} & \textbf{FigureQA} \\
\midrule
Zen-VL      & 82.1 & 71.3 & 84.2 & 87.4 \\
Zen2-VL     & 85.8 & 76.9 & 87.6 & 90.1 \\
\textbf{Zen3-VL} & \textbf{89.2} & \textbf{82.4} & \textbf{91.3} & \textbf{93.6} \\
\bottomrule
\end{tabular}
\label{tab:chart}
\end{table}

\subsection{Scientific Figure Understanding}

\begin{table}[H]
\centering
\caption{Scientific figure parsing accuracy (chart-to-table extraction)}
\begin{tabular}{lcccc}
\toprule
\textbf{Chart Type} & \textbf{Extraction F1} & \textbf{Numeric Accuracy} & \textbf{Label Accuracy} \\
\midrule
Bar chart      & 94.2 & 91.8 & 97.3 \\
Line chart     & 91.7 & 88.4 & 96.1 \\
Scatter plot   & 87.3 & 84.9 & 93.8 \\
Pie chart      & 92.8 & 90.3 & 96.7 \\
Heatmap        & 83.4 & 79.1 & 91.2 \\
Box plot       & 85.6 & 82.4 & 94.1 \\
Average        & 89.2 & 86.2 & 94.9 \\
\bottomrule
\end{tabular}
\label{tab:scientific}
\end{table}

\subsection{High-Resolution Evaluation}

\begin{table}[H]
\centering
\caption{Performance vs. input resolution (MMBench, high-resolution subset)}
\begin{tabular}{lccc}
\toprule
\textbf{Resolution} & \textbf{Tiles} & \textbf{Score} & \textbf{Improvement vs. 448px} \\
\midrule
448$\times$448 (1 tile)    & 1   & 87.4 & --- \\
896$\times$896 (4 tiles)   & 4   & 89.6 & +2.2 \\
1792$\times$1792 (16 tiles) & 16 & 91.1 & +3.7 \\
3584$\times$3584 (64 tiles) & 64 & 91.3 & +3.9 \\
\bottomrule
\end{tabular}
\label{tab:resolution}
\end{table}

%% -----------------------------------------------------------------------
\section{Related Work}
\label{sec:related}

\subsection{Vision-Language Pre-Training}

The contrastive vision-language pre-training paradigm established the foundation
for aligning visual and textual representations~\cite{clip}. Subsequent work scaled
both the vision encoder and the language backbone, demonstrating consistent
capability improvements across VQA, image captioning, and visual reasoning.

\subsection{Document Understanding}

Document AI has evolved from OCR-focused pipelines to end-to-end transformer
architectures that jointly understand text, layout, and visual elements~\cite{layoutlm}.
Zen3-VL extends this by integrating document understanding capabilities into a
general-purpose VL model rather than maintaining separate document-specific checkpoints.

\subsection{Chart Understanding}

Chart question answering has been benchmarked extensively since ChartQA~\cite{chartqa},
with models progressing from template matching to learned visual reasoning. The
Zen3-VL chart-to-data synthetic pipeline scales training data generation beyond what
manual annotation can provide, enabling high-accuracy structured extraction.

%% -----------------------------------------------------------------------
\section{Conclusion}
\label{sec:conclusion}

Zen3-VL establishes new state-of-the-art results across all five primary
vision-language benchmarks (MMMU 72.1\%, MMBench 91.3\%, DocVQA 94.8\%,
ChartQA 89.2\%, TextVQA 85.4\%) through a combination of the 72B Zen MoDE backbone,
high-resolution dynamic tiling, and a document-specialized vision encoder.

The scientific figure parser and chart-to-data synthetic pipeline represent
particularly novel contributions, enabling structured data extraction from scientific
literature at scale --- a capability with significant downstream value for
research automation and knowledge graph construction.

Future work will target video document understanding (slide decks, lecture recordings)
and extend the structured output schema to cover 3D plots, network diagrams, and
biological pathway maps.

\section*{Acknowledgments}

The authors thank Zoo Labs Foundation for benchmark curation, scientific figure
dataset annotation, and compute allocation for the high-resolution encoder training.

\bibliographystyle{plain}
\begin{thebibliography}{9}

\bibitem{clip}
A. Radford et al.,
``Learning Transferable Visual Models From Natural Language Supervision,''
\textit{ICML}, 2021.

\bibitem{layoutlm}
Y. Xu et al.,
``LayoutLM: Pre-Training of Text and Layout for Document Image Understanding,''
\textit{KDD}, 2020.

\bibitem{chartqa}
A. Masry et al.,
``ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning,''
\textit{ACL Findings}, 2022.

\bibitem{zip007}
Zoo Labs Foundation,
``ZIP-007: BitDelta --- Quantization-Aware Delta Compression for Language Models,''
\textit{Zoo Improvement Proposals}, 2024.

\end{thebibliography}

\end{document}