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\title{Do Biological Abstractions Generalize to Meta-Level AI Systems Engineering?\\
\large Evidence from Evolutionary Configuration Search in Operon}

\author{Bogdan Banu}

\date{April 2026}

\begin{document}

\maketitle

\begin{abstract}
We test whether biological abstractions designed for running multi-agent
organisms also generalize to \emph{evolving} their configurations.
Using Operon's biomimetic framework, we implement an evolution loop that
maps candidate configurations to Genome objects, detects stagnation via
an extended EpiplexityMonitor, and compares tournament mutation against
LLM-backed proposal with rich filesystem context.

Our findings are mixed: biological abstractions generalize cleanly as
\textbf{code structure} (lossless Genome round-trip in 5~lines,
scale-invariant epistemic health monitoring) but \textbf{not} as
optimization algorithms (tournament mutation matches or outperforms LLM
reasoning across config-space and topology-space search). Rich context
improves the LLM proposer $3\times$ over compressed history ($0.49$ vs
$0.15$), consistent with the Meta-Harness insight, but does not dominate
blind mutation ($0.49$ vs $0.44$). Topology mutations improve tournament
($0.60$) but degrade the LLM proposer ($0.36$). We relate our
architecture to the categorical framework of de~los~Riscos et~al.\ and
argue that the structural guarantee features (immune systems,
developmental gating, epistemic monitoring) remain the core value
proposition of biology-inspired agent engineering.
\end{abstract}

\section{Introduction}

Biology-inspired abstractions have been proposed for structuring AI agent
systems~\cite{banu2026operon}. Operon implements genes, genomes,
epiplexity monitoring, immune systems, and developmental gating as
engineering primitives for multi-agent organisms. Phases C1--C7 validated
these abstractions at the organism level: adapters bridge six external
frameworks, four TLA+ specifications verify safety invariants, and
compilers translate organisms across deployment targets.

Phase~C8 asks a different question: do these abstractions generalize to
the \emph{meta-level}---evolving organisms, not just running them? This
is the natural extension of the biological metaphor: if organisms have
genomes, can we evolve those genomes to discover better configurations?

We test this by implementing a meta-harness that:
\begin{enumerate}
\item Maps \texttt{CandidateConfig} objects to \texttt{Genome} via the
    existing gene abstraction
\item Uses \texttt{EpiplexityMonitor} with a pluggable
    \texttt{DistanceProvider} for stall detection
\item Wraps each evolution step as a Zardini \texttt{DesignProblem}
\item Compares tournament mutation against an LLM proposer with
    filesystem-backed context (following the Meta-Harness
    insight~\cite{lee2026metaharness})
\end{enumerate}

\section{Related Work}

Ao et~al.~\cite{ao2026delegation} prove that without exogenous signals,
delegated multi-agent networks are dominated by centralized baselines.
Lee et~al.~\cite{lee2026metaharness} show that giving proposers
filesystem access to all prior candidates (${\sim}10$~MTok/iteration)
dramatically outperforms compressed-feedback optimizers. de~los~Riscos
et~al.~\cite{delosriscos2026categorical} provide a category-theoretic
framework (\textsc{ArchAgents}) where architectures are objects,
translations are morphisms, and agents are monoidal functors---mapping
tightly to Operon's organism/compiler/adapter ecosystem.

\section{Method}

\subsection{Genome Mapping}

Each \texttt{CandidateConfig} (stage modes, models, thresholds) is
flattened into \texttt{Gene} objects with \texttt{GeneType.STRUCTURAL}
for mode/model and \texttt{GeneType.REGULATORY} for visibility flags.
The round-trip \texttt{candidate\_to\_genome} $\rightarrow$
\texttt{genome\_to\_candidate} is lossless---5~lines of flattening
logic, no type coercion. This is the strongest evidence that the gene
abstraction generalizes beyond its original per-agent configuration
use case.

\subsection{Epistemic Health Monitoring}

The existing \texttt{EpiplexityMonitor} (Bayesian surprise via embedding
cosine similarity) is extended with a \texttt{DistanceProvider} protocol.
For configuration space, \texttt{ConfigHammingDistance} measures
field-level mismatches. The core formula ($\hat{E}_t = \alpha \cdot
\text{novelty} + (1-\alpha) \cdot \text{perplexity}$) is unchanged---only
the novelty source is pluggable. The monitor triggers
\textsc{Stagnant}/\textsc{Exploring} transitions identically to the
embedding path, confirming scale-invariance.

\subsection{Dual Stall Detection}

Two independent signals trigger the LLM proposer:
\begin{enumerate}
\item \textbf{Config novelty stall} (EpiplexityMonitor): consecutive
    similar configurations
\item \textbf{Score plateau}: best score not improved in
    $\text{threshold} \times |\text{tasks}|$ steps
\end{enumerate}
The second signal was necessary because tournament mutations always
produce novel configs (high novelty) even when scores stagnate---the
epiplexity-only check never fired.

\subsection{Proposer Strategies}

\textbf{Tournament mutation} (70\%): select top-$k$, mutate one random
field via \texttt{Genome.mutate()}. The mutation handles discrete values
naturally---the gap in \texttt{Genome.replicate()} (numeric jitter only)
does not affect explicit targeted mutations.

\textbf{LLM proposer} (30\%, on stall): reads full candidate configs +
execution trace metadata from filesystem store. Gemini-2.5-flash with
4096~token budget (truncation at 2000 was the hidden bug that masked all
early results).

\subsection{Topology Mutations (Phase~B)}

Extended \texttt{CandidateConfig} with \texttt{edges} (directed wiring
pairs) and three mutation operators: \texttt{add\_stage},
\texttt{remove\_stage}, \texttt{rewire}. DAG execution via
\texttt{WiringDiagram} + \texttt{ResourceAwareExecutor} (parallel groups
via \texttt{ThreadPoolExecutor}).

\section{Results}

\subsection{Phase~A: Configuration Search}

\begin{table}[h]
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Path} & \textbf{Mean Score} & \textbf{N} \\
\midrule
Tournament mutation & 0.44 & 9 \\
LLM proposal (parsed) & 0.49 & 24 \\
LLM proposal (fallback to tournament) & 0.41 & 7 \\
\midrule
\emph{Overall LLM path} & \emph{0.47} & \emph{31} \\
\bottomrule
\end{tabular}
\caption{Phase~A: config-only evolution with rich filesystem context
(4 tasks, 10 iterations, 40 assessments). Tournament runs first (9
steps before stall threshold), then the LLM proposer dominates. An
earlier exploratory run with compressed context (index entries only)
yielded LLM scores of $0.15$ (n=2), suggesting rich context is
important, though the small sample precludes strong claims.}
\end{table}

\subsection{Phase~B: Topology Search}

\begin{table}[h]
\centering
\begin{tabular}{lccc}
\toprule
\textbf{Proposer} & \textbf{Phase~A} & \textbf{Phase~B} & \textbf{$\Delta$} \\
\midrule
Tournament & 0.44 (n=9) & 0.60 (n=9) & +0.16 \\
LLM (overall path) & 0.47 (n=31) & 0.36 (n=31) & $-$0.11 \\
\bottomrule
\end{tabular}
\caption{Phase~A (config-only) vs Phase~B (topology + config + DAG
execution). Tournament N is consistent (first 9 pre-stall steps). LLM
includes both successful parses and fallbacks. Tournament \emph{improves}
with topology; the LLM path \emph{degrades}.}
\end{table}

\subsection{Abstraction Quality}

\begin{table}[h]
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Abstraction} & \textbf{Generalizes?} & \textbf{Evidence} \\
\midrule
Genome $\rightarrow$ CandidateConfig & Yes & Lossless round-trip, 5 lines \\
EpiplexityMonitor + DistanceProvider & Yes & Scale-invariant transitions \\
DesignProblem wrapping & Yes & Natural composition \\
\texttt{feedback\_fixed\_point} & No & Evolution $\neq$ convergent iteration \\
TrustRegistry & No & Overkill for 2 proposers \\
\bottomrule
\end{tabular}
\caption{Biological abstraction generalization results.}
\end{table}

\section{Discussion}

\subsection{Code Structure vs.\ Optimization}

The central finding: biological abstractions generalize as \emph{code
structure} but not as \emph{optimization algorithms}. The Genome mapping,
EpiplexityMonitor extension, and DesignProblem wrapping produce clean,
composable code. But the evolutionary search itself---mutation, selection,
population management---does not outperform random tournament mutation.

This is consistent with Ao et~al.: without genuinely new exogenous
signals, the multi-agent (LLM proposer) approach cannot beat the
single-agent (tournament) baseline. Rich context helps ($3\times$), but
the search space (config knobs) is too small for LLM reasoning to
provide structural advantage over random exploration.

\subsection{Categorical Interpretation}

In the de~los~Riscos framework, Phase~A explores agents within a fixed
architecture (knowledge-layer changes: $\text{Know}_A$), while Phase~B
explores architecture morphisms (moving between objects in
\textsc{ArchAgents}). Our finding that topology mutations improve
tournament but degrade LLM suggests that the categorical structure
constrains useful mutations---random morphisms that happen to preserve
feasibility are more productive than reasoned morphisms that optimize
inert structure.

\subsection{Implications}

The structural guarantee features---immune systems, epiplexity monitoring,
developmental gating---remain the core value proposition. C8 confirms
they should be the focus of empirical validation, not meta-optimization.
The evolution loop infrastructure is useful as an evaluation tool (moved
to \texttt{eval/meta/}) but does not belong in the structural guarantee
library.

\section{Conclusion}

Biological abstractions in AI systems engineering generalize to the
meta-level as organizing principles, not as optimization advantages. The
gene abstraction covers configuration space. Epistemic health monitoring
is scale-invariant. Co-design composition works at the meta-level. But
evolution does not beat random mutation for the search spaces we tested.
The right direction for biology-inspired AI engineering is measuring
structural safety guarantees, not optimizing search.

\section*{Data Availability}

The evolution loop, proposers, and evaluation harness are open source at
\url{https://github.com/coredipper/operon} under \texttt{eval/meta/}.
Run data (candidate configs, execution traces, assessment indices) is
generated by \texttt{run\_meta\_evolution.py} and persisted to
\texttt{.operon/evolution/}. The 57 C8-specific tests are in
\texttt{tests/eval/test\_meta\_harness.py}. Install: \texttt{pip install
operon-ai==0.26.0}.

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