papers/hanzo-dso/sections/01-introduction.tex

\section{Introduction}

\label{sec:intro}

The Active Semantic Optimization (ASO) framework~\cite{hanzo2026aso}
demonstrates that frozen language models can be adapted to new tasks via
compressed experiential priors applied at decode time. However, ASO operates in
isolation: each agent builds its own prior bank from its own rollouts, unable to
benefit from the experiences of other agents working on similar tasks.

Traditional federated learning~\cite{mcmahan2017communication} addresses
multi-agent learning by aggregating gradients or model parameters, but this
approach is fundamentally incompatible with training-free adaptation.
Parameter-efficient methods like federated LoRA~\cite{zhang2024fedlora}
reduce communication costs but still require gradient computation on each
participant.

\paragraph{The Sharing Problem.}
Consider $n$ agents independently running ASO on related tasks. Each produces
compressed priors encoding what works and what does not for their specific
sub-problems. These priors contain transferable knowledge: an agent that
discovers ``always handle \texttt{None} inputs before processing'' benefits all
agents working on similar Python code. Yet without a sharing mechanism, this
knowledge remains siloed.

\paragraph{The Trust Problem.}
In a decentralized setting, some participants may be Byzantine: submitting
random noise, targeted poisoning attacks, or Sybil identities that vote for
malicious priors. Any aggregation protocol must be robust to such adversarial
behavior while still extracting value from honest contributions.

\paragraph{Our Contribution.}
DSO solves both problems simultaneously via:
\begin{enumerate}[leftmargin=1.5em]
    \item A three-layer storage architecture separating integrity (on-chain),
          availability (IPFS/Arweave), and performance (local replicas).
    \item Byzantine-robust weighted median aggregation with formal fault
          tolerance guarantees.
    \item Sybil-resistant gossip with stake-weighted propagation and
          quality-gated filtering.
    \item Integration with Proof-of-AI (PoAI) consensus for attestation-based
          quality verification and incentive alignment.
\end{enumerate}

\paragraph{Paper Outline.}
Section~\ref{sec:background} provides background on ASO and Byzantine consensus.
Section~\ref{sec:architecture} describes the three-layer storage architecture.
Section~\ref{sec:protocol} specifies the DSO protocol.
Section~\ref{sec:aggregation} formalizes Byzantine-robust aggregation.
Section~\ref{sec:sybil} analyzes Sybil resistance.
Section~\ref{sec:gossip} specifies the gossip protocol.
Section~\ref{sec:convergence} proves convergence results.
Section~\ref{sec:incentives} describes incentive mechanisms.
Section~\ref{sec:experiments} presents experimental evaluation.
Section~\ref{sec:related} discusses related work.
Section~\ref{sec:conclusion} concludes.

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