papers/hanzo-aso/hanzo-aso.tex

% Hanzo ASO (Active Semantic Optimization) Paper
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\title{Active Semantic Optimization: Training-Free Adaptation\\via Bayesian Product-of-Experts Decoding}
\author{
    Antje Worring, Hanzo AI Research \\
    Hanzo AI Research\\
    \textit{Hanzo AI Inc (Techstars '17), San Francisco, CA}\\
    \texttt{[email protected]}
}
\date{September 2025}

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% ABSTRACT
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\begin{abstract}
We present \textbf{Active Semantic Optimization (ASO)}, a training-free adaptation
framework that achieves performance competitive with fine-tuning at a fraction of
the cost (\$18 vs.\ \$10{,}000+). ASO combines Training-Free Group-Relative Policy
Optimization (TF-GRPO) with decode-time Product-of-Experts (PoE) ensemble to
adapt frozen large language models to new tasks without any gradient computation.
The key insight is that \emph{semantic advantages}---structured descriptions of
what makes one solution better than another---can be extracted from grouped
rollouts and applied as multiplicative token-level prior factors during
autoregressive decoding.

We formalize ASO as a Bayesian Product-of-Experts model where each experiential
prior contributes a factor $\phi_m(y_t \mid x, y_{<t})$ to the decoding
distribution, with closed-form expert weights $\eta_m \propto q_m / (1 - q_m)$
derived from quality attestation reliability. We prove that the PoE posterior
converges to the Bayes-optimal predictor under mild regularity conditions and
that the 1-bit compressed prior representation preserves convergence guarantees
with bounded approximation error.

Empirical evaluation across three code generation benchmarks demonstrates
consistent improvements: \textbf{72.8\%} on HumanEval (+9.1\% over baseline),
\textbf{68.4\%} on MBPP (+7.2\%), and \textbf{18.2\%} on SWE-bench Verified
(+5.7\%). Ablation studies confirm that each component---TF-GRPO rollouts, PoE
decoding, epistemic exploration, and 1-bit compression---contributes
meaningfully to the final result. The total adaptation cost for a new domain is
approximately \$18 in API calls, compared to \$10{,}000--\$50{,}000 for
LoRA/QLoRA fine-tuning and \$100{,}000+ for full RLHF.
\end{abstract}

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\input{sections/06-convergence-analysis}
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\input{sections/08-hanzo-dev-agent-architecture}
\input{sections/09-experimental-results}
\input{sections/10-related-work}
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