papers/zen/zen3-omni_whitepaper.tex

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\title{\textbf{Zen3-Omni: Third-Generation Unified Multimodal Intelligence}\\
\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-Omni advances multimodal AI by unifying text, image, audio, and video understanding
in a single 7B parameter model, featuring a novel cross-modal attention architecture that
enables fluent reasoning across modalities with state-of-the-art performance on multimodal
benchmarks. The third generation introduces substantial improvements in video understanding
through temporal sparse attention and real-time audio processing via streaming CTC decoders
integrated natively into the Zen MoDE (Mixture of Distilled Experts) backbone. Zen3-Omni
achieves MMBench 86.2\%, AudioCaps CLAP 0.523, VideoQA 79.4\%, and OCRBench 82.1\%,
making it the leading 7B-class multimodal model across all four primary modalities
simultaneously. The model supports streaming inference for real-time audio/video inputs
with end-to-end latency below 300ms on a single A100 GPU.
\end{abstract}

\tableofcontents
\newpage

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

The dominant paradigm in multimodal AI has been modality-specialized towers: separate
vision encoders, audio encoders, and video encoders, each pre-trained independently and
fused into a shared language model backbone via adapter layers. While effective, this
architecture introduces seams at modality boundaries --- the model reasons about each
modality separately rather than in a unified representational space.

Zen3-Omni is built on a fundamentally different premise: a single cross-modal attention
mechanism processes tokens from all modalities together, allowing the model to form
joint representations where a spoken word, its transcript, and a visual depiction of the
concept co-attend to one another naturally.

This paper makes the following contributions:

\begin{enumerate}
\item \textbf{Unified cross-modal attention} --- A single attention mechanism that handles
      text, image patch, audio frame, and video temporal tokens without modality-specific
      routing, enabling true multi-modal chain-of-thought reasoning.
\item \textbf{Temporal sparse attention for video} --- A hierarchical temporal attention
      pattern that processes 60fps video at 1-second granularity without quadratic
      sequence length blowup, enabling up to 10-minute video inputs.
\item \textbf{Streaming CTC audio decoder} --- Real-time audio transcription and
      understanding with 40ms chunk processing latency, enabling live speech input.
\item \textbf{Third-generation Zen MoDE scaling} --- Expert routing updated with
      modality-aware load balancing that prevents expert collapse on underrepresented
      modalities.
\end{enumerate}

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

\subsection{Unified Token Space}

All four modalities are projected into a shared 4096-dimensional token space before
entering the transformer backbone:

\textbf{Text}: Standard BPE tokenization with a 150,528-token vocabulary. Each token
$t_i \in \mathbb{R}^{4096}$.

\textbf{Image}: ViT-style patchification at 14$\times$14 pixel patches. High-resolution
images use dynamic resolution tiling (up to 4$\times$4 tiles at 448$\times$448 base
resolution). Each image produces 256--4096 patch tokens.

\textbf{Audio}: Mel spectrogram with 128 mel bins, 10ms hop, processed in 40ms chunks.
A 3-layer CNN produces audio tokens at 40ms granularity. 30 seconds of audio yields
750 tokens.

\textbf{Video}: Frame sampling at 1 fps for general understanding, 4 fps for action
recognition. Each frame is patchified identically to images. Temporal position embeddings
encode frame index alongside 2D spatial position.

The unified sequence for a multimodal input is:

\begin{equation}
\mathcal{S} = [t_1, \ldots, t_T] \oplus [v_1, \ldots, v_P] \oplus [a_1, \ldots, a_A] \oplus [\text{vid}_1, \ldots, \text{vid}_F]
\end{equation}

where $\oplus$ denotes concatenation with modality type embeddings prepended.

\subsection{Cross-Modal Attention}

The core innovation is a modified attention mechanism that allows unrestricted
cross-modal attention within the same sequence. Rather than restricting vision
tokens to attend only to vision tokens, Zen3-Omni allows each token to attend
across the full multimodal context:

\begin{equation}
\text{Attention}(Q, K, V) = \text{softmax}\!\left(\frac{QK^T}{\sqrt{d_k}} + M_{\text{causal}}\right) V
\end{equation}

where $M_{\text{causal}}$ is a causal mask that prevents future text tokens from
attending to past text tokens but permits bidirectional attention between vision,
audio, and video tokens (which are treated as context, not generated sequence).

\subsection{Temporal Sparse Attention for Video}

Attending over 10-minute video at 1fps produces sequences of $600 \times 256 = 153{,}600$
vision tokens, making full attention intractable. Zen3-Omni uses a hierarchical
temporal attention pattern:

\begin{itemize}
\item \textbf{Local window}: Each frame attends fully to adjacent $\pm$2 frames.
\item \textbf{Strided global}: Every 10th frame attends to global frame summaries
      (mean-pooled representations of 10-frame segments).
\item \textbf{Query-guided retrieval}: Text query tokens attend to all frame summary
      tokens, then retrieve top-$k$ full-resolution frames for dense attention.
\end{itemize}

This reduces video attention from $O(F^2 P^2)$ to $O(F P^2 + F^2 / s)$ where $s=10$
is the stride factor.

\subsection{Zen MoDE Expert Routing with Modality Balance}

The feed-forward layers use Zen MoDE expert routing with 64 experts, top-4 active
per token. Third-generation routing adds a modality balance loss:

\begin{equation}
\mathcal{L}_{\text{balance}} = \alpha \sum_{e=1}^{64} \left( f_e - \frac{1}{64} \right)^2 + \beta \sum_{m \in \mathcal{M}} \text{KL}(p_m^e \| \bar{p}^e)
\end{equation}

where $f_e$ is the fraction of tokens routed to expert $e$, $p_m^e$ is the routing
distribution for modality $m$, and $\bar{p}^e$ is the average. This prevents
individual experts from specializing exclusively on high-frequency text tokens,
maintaining expert utilization across all modalities.

\subsection{Streaming CTC Audio Decoder}

For real-time audio, Zen3-Omni maintains a parallel CTC head that produces
transcript hypotheses at 40ms intervals. The CTC output is fed back as text tokens
into the main transformer context, enabling the model to reason over audio content
while it is still being received.

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

\subsection{Pre-Training Data}

Zen3-Omni pre-training uses a multimodal corpus of 3T text tokens and aligned
multimodal data:

\begin{itemize}
\item Text: 1.8T tokens (same pipeline as Zen3-Small base model)
\item Image-text pairs: 1.2B pairs from web alt-text, academic figures, and curated datasets
\item Audio-text pairs: 120M samples (speech recognition, audio captioning, music description)
\item Video-text pairs: 85M clips from instructional video, film, and synthetic data
\item Interleaved documents: 400M multimodal documents with mixed modalities in context
\end{itemize}

\subsection{Training Curriculum}

\textbf{Phase 1 --- Modality encoders}: Train image, audio, and video encoders
independently with frozen LM backbone. Duration: 200B image tokens, 50B audio tokens,
20B video tokens.

\textbf{Phase 2 --- Unified pre-training}: Jointly train all components on the full
interleaved corpus with cross-modal attention enabled. Duration: 1.5T tokens equivalent.

\textbf{Phase 3 --- Instruction tuning}: Fine-tune on 80M multimodal instruction samples
covering all four modality combinations.

\textbf{Phase 4 --- RLHF}: Preference optimization using a multimodal reward model
trained on human ratings of response quality across all modality inputs.

\subsection{Distillation from Zen3-VL}

For vision-language tasks, Zen3-Omni uses Zen3-VL (72B) as a teacher, distilling
visual understanding capabilities via logit matching on image-text pairs. This
bridges the 10$\times$ parameter gap and brings Zen3-Omni VL performance within
5\% of the 72B flagship.

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

\subsection{Vision-Language}

\begin{table}[H]
\centering
\caption{Vision-language benchmarks}
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{Params} & \textbf{MMBench} & \textbf{MMMU} & \textbf{TextVQA} & \textbf{OCRBench} \\
\midrule
Zen-Omni     & 7B & 78.3 & 58.4 & 74.1 & 71.2 \\
Zen2-Omni    & 7B & 82.1 & 63.7 & 79.8 & 77.6 \\
\textbf{Zen3-Omni} & \textbf{7B} & \textbf{86.2} & \textbf{68.9} & \textbf{83.4} & \textbf{82.1} \\
\bottomrule
\end{tabular}
\label{tab:vl}
\end{table}

\subsection{Audio Understanding}

\begin{table}[H]
\centering
\caption{Audio benchmarks}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{AudioCaps CLAP} & \textbf{LibriSpeech WER} & \textbf{MSVD-QA} & \textbf{AIR-Bench} \\
\midrule
Zen-Omni     & 0.481 & 3.8 & 61.3 & 71.4 \\
Zen2-Omni    & 0.502 & 3.2 & 67.9 & 76.8 \\
\textbf{Zen3-Omni} & \textbf{0.523} & \textbf{2.7} & \textbf{73.2} & \textbf{81.9} \\
\bottomrule
\end{tabular}
\label{tab:audio}
\end{table}

\subsection{Video Understanding}

\begin{table}[H]
\centering
\caption{Video understanding benchmarks}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{VideoQA} & \textbf{EgoSchema} & \textbf{MVBench} & \textbf{Video-MME} \\
\midrule
Zen-Omni     & 68.4 & 52.1 & 71.2 & 61.3 \\
Zen2-Omni    & 73.8 & 59.4 & 76.9 & 68.7 \\
\textbf{Zen3-Omni} & \textbf{79.4} & \textbf{66.2} & \textbf{83.1} & \textbf{74.8} \\
\bottomrule
\end{tabular}
\label{tab:video}
\end{table}

\subsection{Cross-Modal Reasoning}

\begin{table}[H]
\centering
\caption{Cross-modal reasoning: tasks requiring simultaneous use of 2+ modalities}
\begin{tabular}{lcc}
\toprule
\textbf{Task} & \textbf{Zen3-Omni} & \textbf{Zen2-Omni} \\
\midrule
Audio-Visual QA (synchronized A+V)   & 74.3 & 63.1 \\
Talking-Head Description (A+V+Text)  & 71.8 & 58.4 \\
Chart with Narration Understanding   & 81.2 & 69.7 \\
Live Lecture Comprehension           & 68.9 & 54.2 \\
\bottomrule
\end{tabular}
\label{tab:crossmodal}
\end{table}

\subsection{Streaming Latency}

\begin{table}[H]
\centering
\caption{End-to-end streaming inference latency (A100 80GB)}
\begin{tabular}{lcc}
\toprule
\textbf{Input Mode} & \textbf{Time to First Token (ms)} & \textbf{Throughput (tok/s)} \\
\midrule
Text only             &  18 & 4200 \\
Image + Text          &  62 & 3800 \\
Audio stream (40ms)   &  43 & 3900 \\
Video (1fps, 60s)     & 210 & 3100 \\
All modalities        & 270 & 2800 \\
\bottomrule
\end{tabular}
\label{tab:latency}
\end{table}

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

\subsection{Multimodal Language Models}

Early multimodal language models relied on frozen vision encoders with lightweight
projection adapters connecting to a text backbone~\cite{llava}. While effective for
image captioning and VQA, these architectures treat vision tokens as second-class
context, limiting deep cross-modal reasoning. Zen3-Omni's unified token space builds
on work showing that full cross-modal attention substantially improves tasks requiring
joint reasoning.

\subsection{Audio-Language Models}

Audio integration with language models has advanced rapidly through mel spectrogram
tokenization and learned audio codebooks. Streaming CTC integration, as used in
Zen3-Omni, was pioneered for real-time speech translation systems and extended here
to general audio understanding within a multimodal context.

\subsection{Video Understanding}

Long-form video understanding remains challenging due to the quadratic attention cost.
Prior work addressed this through frame sampling, visual summarization, and hierarchical
architectures. Zen3-Omni's temporal sparse attention synthesizes these approaches into
a unified mechanism compatible with the Zen MoDE backbone.

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

Zen3-Omni demonstrates that unified cross-modal reasoning in a 7B parameter model
can achieve state-of-the-art results across vision, audio, video, and text simultaneously.
The third-generation Zen MoDE architecture with modality-balanced expert routing,
temporal sparse attention, and streaming CTC decoding addresses the key challenges
of prior generation multimodal models.

Future work will extend to 3D scene understanding, tactile sensor inputs, and
sub-100ms real-time video captioning for accessibility applications.

\section*{Acknowledgments}

The authors thank Zoo Labs Foundation for multimodal dataset curation and compute
allocation, and the Zen LM research community for benchmark contributions.

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

\bibitem{llava}
H. Liu et al.,
``Visual Instruction Tuning,''
\textit{NeurIPS}, 2023.

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

\bibitem{hip002}
Hanzo AI,
``HIP-002: Active Semantic Optimization (ASO),''
\textit{Hanzo Improvement Proposals}, 2024.

\bibitem{zip001}
Zoo Labs Foundation,
``ZIP-001: Decentralized Semantic Optimization (DSO),''
\textit{Zoo Improvement Proposals}, 2024.

\end{thebibliography}

\end{document}