papers/embedding-7680/sections/08-integration-with-zoo-network.tex

\section{Integration with Zoo Network}


\subsection{DSO Experience Retrieval}

Zen Reranker serves as the canonical embedding model for Zoo Network's \DSO{} infrastructure:

\begin{lstlisting}[language=Python,basicstyle=\ttfamily\small]
from zen_reranker import ZenReranker

model = ZenReranker.from_pretrained("zenlm/zen-reranker-8b")

# Embed experience for storage
experience = "When debugging memory leaks in Python..."
embedding = model.encode(experience)  # 7680-dim
compressed = bitdelta_compress(embedding)  # 964 bytes

# Retrieve similar experiences
query = "How to find memory issues?"
query_emb = model.encode(query)
similar = index.search(query_emb, k=5)
\end{lstlisting}

\subsection{Byzantine-Robust Aggregation}

The 7680-dim space enables efficient median computation for Byzantine-robust experience selection:

\begin{equation}
e_{\text{selected}} = \arg\min_{e \in E} \sum_{i} \|\phi(e) - \phi(e_i)\|_2
\end{equation}