papers/hllm-training-free-grpo/sections/09-integration-with-zoo-network.tex

\section{Integration with Zoo Network}

\label{sec:governance}

Training-Free GRPO is designed for deployment on Zoo Network, a decentralized AI/ML blockchain built on Hanzo Network's base compute infrastructure. The HLLM training pipeline composes naturally with the per-LLM chain framework~\cite{zoo-per-llm-chains}: each model whose context is being curated by TF-GRPO has a sovereign Quasar appchain (Lux LP-133) that records experience-library updates as research artifacts, gradient-free though they may be. The chain's deterministic ordering on Quasar 3.0 (Lux LP-020) ensures that every published experience set is reproducible from chain state.

\subsection{Decentralized Compute Layer}

\textbf{Hanzo GPU Nodes:}
\begin{itemize}
    \item Docker-based LLM inference containers
    \item Auto-scaling GPU instances (NVIDIA A100/H100)
    \item Distributed training support (FSDP, DeepSpeed)
    \item Proof of Inference (optional zk-SNARK verification)
\end{itemize}

\textbf{Inference Routing:}
\begin{enumerate}
    \item User submits query to Zoo Network API
    \item Router fetches canonical experience library (IPFS + on-chain root hash)
    \item Semantic search retrieves relevant experiences
    \item Query + experiences routed to available GPU node
    \item Frozen base model executes inference
    \item Response returned with optional cryptographic proof
\end{enumerate}

\subsection{DAO Governance with KEEPER Tokens}

Experience library updates are governed by the Zoo DAO using KEEPER tokens:

\begin{itemize}
    \item \textbf{Proposal}: Contributors submit experience updates (new Merkle root + IPFS CID)
    \item \textbf{Voting}: KEEPER holders vote (quadratic voting to prevent plutocracy)
    \item \textbf{Threshold}: 66\% approval required (2/3 supermajority)
    \item \textbf{Execution}: Approved updates recorded on-chain
    \item \textbf{Rewards}: Contributors earn inference credits + usage royalties
\end{itemize}

\textbf{Quadratic Voting Formula:}
\begin{equation}
\text{VotePower}(x) = \sqrt{\text{KEEPERBalance}(x)}
\end{equation}
This ensures large token holders cannot dominate governance (square root dampening).

\subsection{Economic Model: Contribute → Access}

Users contribute data/experiences to earn future inference rights:

\begin{enumerate}
    \item \textbf{Data Contribution}: Upload training datasets, preference pairs, domain text
    \item \textbf{Experience Curation}: Propose new experiences, vote on updates
    \item \textbf{Quality Signals}: Rank model outputs, flag harmful content
\end{enumerate}

\textbf{Rewards:}
\begin{itemize}
    \item \textbf{Inference Credits}: Free queries proportional to contribution
    \item \textbf{Priority Routing}: Skip queues during high demand
    \item \textbf{Governance Rights}: KEEPER tokens for voting power
    \item \textbf{Usage Royalties}: Revenue share when your experiences are used
\end{itemize}

\subsection{Security and Verification}

\subsubsection{Content-Addressable Storage}
All experiences stored in IPFS/Arweave:
\begin{itemize}
    \item \textbf{Tamper-Proof}: Content hash changes if modified
    \item \textbf{Deduplication}: Identical experiences automatically merged
    \item \textbf{Permanence}: Arweave ensures perpetual availability
\end{itemize}

\subsubsection{Merkle Proofs}
On-chain registry stores only Merkle root (32 bytes):
\begin{itemize}
    \item \textbf{Verification}: Anyone can verify experience inclusion with $\mathcal{O}(\log N)$ proof
    \item \textbf{Audit Trail}: Complete history of library changes recorded
    \item \textbf{Lightweight}: Blockchain stores 32 bytes, not entire library
\end{itemize}

\subsubsection{Frozen Base Model}
Model weights are cryptographically verified:
\begin{itemize}
    \item \textbf{SHA-256 Hash}: Immutable identifier for model version
    \item \textbf{Hugging Face Hub}: Canonical source with Git history
    \item \textbf{No Weight Updates}: Eliminates model poisoning attacks
    \item \textbf{Verifiable Inference}: Optional zk-SNARKs prove (input, model, context) → output
\end{itemize}