papers/hanzo-dso/sections/12-conclusion.tex

\section{Conclusion}

\label{sec:conclusion}

Decentralized Semantic Optimization enables collective model adaptation by
sharing compressed experiential priors across a network of LLM agents. The
three-layer storage architecture balances integrity, availability, and
performance. Byzantine-robust weighted median aggregation provides formal
guarantees under adversarial conditions. Sybil resistance via stake-weighted
quality gating prevents identity-based attacks.

\paragraph{Key Results.}
\begin{itemize}[leftmargin=1.5em]
    \item 15.2\% improvement in multi-agent code generation vs.\ isolated agents.
    \item Resilience to 20\% Byzantine participants (degradation $< 0.9\%$).
    \item $29.3\times$ storage compression via 1-bit quantization.
    \item Logarithmic propagation latency scaling with network size.
\end{itemize}

\paragraph{Limitations.}
DSO requires at least $2f + 1$ honest participants for guaranteed correctness.
The quality verification relies on holdout benchmarks, which may not represent
all task types. Cross-domain transfer (e.g., Python $\to$ Rust) remains limited.

\paragraph{Future Work.}
Cross-domain prior transfer via embedding alignment, hierarchical aggregation
with specialized sub-groups, privacy-preserving aggregation via secure
multi-party computation, and integration with Hamiltonian Market Maker (HMM) for
pricing prior contributions.

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