\section{Discussion}\label{sec:discussion}

\subsection{When Biological Structure Earns Complexity}

The two positive results share a common pattern: both work through
\emph{mechanism design} rather than \emph{algorithmic sophistication}.

The metabolic priority gate is a simple state machine.  When resources
drop below 10\%, operations with priority $< 5$ are rejected.  This is
not a complex algorithm---it is a structural rule.  But it provides a
hard guarantee: critical operations \emph{will} be served under any
load pattern, at the cost of bounded throughput reduction.  A flat
counter cannot provide this guarantee without reimplementing the state
machine, at which point it is no longer a flat counter.

The quorum sensing decay is exponential half-life applied to signal
concentrations.  Again, not algorithmically complex.  But the
combination of continuous signals (noise averaging) and temporal decay
(stale evidence removal) occupies a precision--recall position that
neither majority voting nor independent detection can reach without
adding similar structural properties.

In both cases, the biological metaphor provided the \emph{right
structural choice}---priority-dependent resource gating from metabolic
state machines, temporal signal decay from autoinducer kinetics---rather
than a better algorithm for the same structural choice.

\subsection{When Embedding Quality Matters}

The epiplexity result demonstrates a subtler lesson than the other two
benchmarks: biological designs that rely on \emph{information quality}
succeed only when that quality is present.

With mock embeddings, the two-signal design fails---the novelty signal
carries no semantic information, so combining it with approximate
perplexity produces worse results than a single clean cosine-similarity
signal.  With real sentence embeddings (all-MiniLM-L6-v2), the same
design dominates on convergence discrimination: 96\% accuracy versus
2--40\% for the naive detector on the hardest scenarios.

The structural guarantee here is conditional: the Bayesian two-signal
design provides a \emph{convergence/stagnation distinction guarantee}
that the cosine detector structurally cannot make, but only when the
embedding signal carries semantic meaning.  This is analogous to a
biological receptor that provides a structural binding guarantee but
only functions in the presence of its ligand.

This refines the C8 finding~\cite{banu2026operon} that biological
abstractions generalize as code structure rather than optimization
algorithms.  The epiplexity monitor's \emph{structure} (two independent
signals combined via Bayesian weighting) does generalize---but its
\emph{performance} depends on signal quality.

The pattern across all three benchmarks: biological designs that enforce
structural invariants outperform simpler alternatives, while
information-processing gains depend on signal quality.

\subsection{Implications for Framework Integration}

Operon provides convergence adapters for six agent
frameworks~\cite{banu2026convergence}.  The benchmark results inform
which frameworks benefit most from biological structural guarantees:

\paragraph{A-Evolve.}
Its single-agent Solve-Observe-Evolve-Gate-Reload loop is where
metabolic budgets directly prevent unbounded exploration.  The
benchmark shows the fitness gate (a critical operation) is always
evaluated under metabolic budgeting---mapped to A-Evolve, this means
the evolutionary quality check is never skipped even when the
exploration budget is depleted.

\paragraph{Swarms.}
Its graph-based multi-agent topology is where quorum sensing provides
the most value: leaderless consensus with zero false positives.  When
false consensus triggers coordinated action across a swarm, the 0\%
FPR guarantee is operationally critical.

\paragraph{DeerFlow.}
Its hierarchical architecture with recursive sub-agent calls benefits
from metabolic budgets that prevent resource exhaustion.  The
MTORScaler's rate-of-change sensitivity is especially relevant for
DeerFlow's progressive skill loading, where resource consumption can
accelerate suddenly.

\paragraph{AnimaWorks, Ralph, Scion.}
Lighter benefits: genome immutability for configuration audit
(AnimaWorks), metabolic budgets mapping to backpressure enforcement
(Ralph), and quorum sensing for cross-container consensus (Scion).

\subsection{Composition Non-Interference}

Ma et~al.~\cite{ma2026atomic} show that five atomic coding skills
(localize, edit, test, reproduce, review) compose without negative
interference under joint RL training.  We ran a limited comparison
using Operon's skill composition machinery with a real LLM (Gemma~4
27B via Ollama) on three bug-fix tasks (SQL injection, off-by-one
pagination, TOCTOU race condition), 3 repetitions each.  This tests
3 of 5 skills via prompt composition (not joint RL), so the comparison
is narrower than Ma et~al.'s original setting.

\begin{table}[h]
\caption{Composition non-interference: individual skill quality vs
composed localize$\to$edit$\to$test pipeline.  $\Delta$ =
composed $-$ mean(individual).  Threshold: $|\Delta| \leq 0.1$
= no interference.}
\label{tab:composition}
\centering
\small
\begin{tabular}{lcccl}
\toprule
Task & mean(indiv) & composed & $\Delta$ & Verdict \\
\midrule
sql\_injection & 0.806 & 0.967 & $+0.161$ & positive \\
off\_by\_one & 0.900 & 0.993 & $+0.093$ & none \\
race\_condition & 0.517 & 0.350 & $-0.167$ & \textbf{negative} \\
\midrule
Overall & 0.741 & 0.770 & $+0.029$ & none \\
\bottomrule
\end{tabular}
\end{table}

The overall result matches the direction of Ma et~al.'s finding:
composition does not degrade quality on average ($\Delta = +0.029$).
However, the race\_condition task shows negative interference
($\Delta = -0.167$), suggesting that composition can hurt on
genuinely hard problems where intermediate stage outputs mislead
later stages.  The baseline (raw LLM call, quality 0.961) still
dominates in absolute terms---a single capable model answering the
full task outperforms a three-stage pipeline---consistent with
the Ao et~al. finding~\cite{ao2026delegation} that delegation cannot
beat a centralized baseline without exogenous signals.

Individual skill quality varies by task difficulty.  On easier tasks
(SQL injection, off-by-one), ``localize'' and ``edit'' score
0.80--1.00 on average, while on the harder race\_condition task,
mean ``edit'' quality drops to 0.37 and mean ``test'' to 0.42,
with both reaching 0.0 in their worst repetitions.  ``test''
consistently scores lowest across tasks because test-writing in
isolation lacks the context of what was found and fixed---an
expected consequence of skill decomposition where some skills
are context-dependent.

\subsection{Relationship to Epistemic Topology}

Operon's epistemic topology layer derives four bounds from wiring
diagram structure: error amplification, sequential communication
overhead, parallel speedup, and tool density scaling.  These
operate at a different level than the mechanism benchmarks presented
here.

The epistemic bounds predict architecture-level properties (a long
pipeline \emph{will} accumulate sequential overhead); the mechanism
benchmarks test feature-level guarantees (critical operations
\emph{will} be served under pressure).  The two are complementary:
epistemic topology identifies architectures likely to need structural
guarantees; the mechanism benchmarks validate that those guarantees
work as claimed.

Section~\ref{sec:topology-validation} validates the error amplification
bound against measured LLM execution: the number of pipeline stages
strongly predicts execution failure ($\rho = +0.751$, $p < 0.001$),
though the dominant failure mode is timeout rather than quality
degradation.  The epistemic and mechanism layers operate at different
levels of abstraction: topology predicts structural difficulty (more
stages $\Rightarrow$ more fragile), while mechanism benchmarks enforce
feature-level invariants (priority gates, signal decay) that deliver
regardless of pipeline length.

\subsection{Categorical Certificates for Structural Guarantees}

The quorum sensing threshold calibration provides a concrete instance
of \emph{semantic certificates} as formalized by de~los~Riscos
et~al.~\cite{delosriscos2026categorical}.  The no-false-activation
guarantee is a certificate $\text{cert}_\text{QS} = (T, \tau, \text{evds})$
where $T$ states that normal traffic never triggers activation,
$\tau$ maps the theorem's symbols to the quorum sensing parameters
$(N, s, h, m)$, and the evidence is the derivation:

\[
  c_\text{ss} = \frac{N \cdot s}{1 - 2^{-1/h}}, \quad
  \theta = c_\text{ss} \cdot m, \quad
  \text{level} = \frac{c_\text{ss}}{\theta} = \frac{1}{m} < 1.
\]

Because the threshold is defined in terms of architecture parameters
$(N, s, h)$ rather than a fixed constant, the guarantee preserves
under architecture morphisms that change $N$---for instance, when a
convergence compiler maps an Operon organism to a Swarms workflow
with a different agent count.  This is a concrete instance of
Proposition~5.1: structural properties are functorially stable under
architectural refinement.

This pattern---deriving thresholds from steady-state dynamics rather
than hand-tuning constants---extends to other structural guarantees.
The metabolic priority gate's STARVING threshold (10\% of capacity)
could similarly be formalized as a certificate whose evidence is the
state machine's priority rejection rule.  The epiplexity monitor's
convergence/stagnation discrimination threshold could be certified
relative to the embedding model's similarity distribution.

The companion work~\cite{banu2026harness} carries this categorical
framing one level further, formalizing agent harness engineering through
the Architecture triple $(G, \mathrm{Know}, \Phi)$ in which each of the
certificates above is a $\mathrm{Know}$-component object preserved under
compiler functors targeting external frameworks (Swarms, DeerFlow, Ralph,
Scion, LangGraph).  Paper~5 supplies the language for \emph{what
survives compilation}; the three benchmarks reported here supply the
empirical evidence that the substrate-independent guarantees so named
are non-vacuous at the bio-motif layer.  The two papers are designed to
be read as a complementary pair: Paper~5 establishes the categorical
scaffolding, while this work measures whether the specific surfaces
(metabolism, quorum sensing, epiplexity) earn the complexity their
biological grounding suggests.

\subsection{Structural Guarantees by Layer}

The end-to-end evaluation (Section~\ref{sec:e2e-eval}) reveals that
structural guarantee value is layer-dependent:

\paragraph{State layer (deterministic).}
DNA repair provides 100\% corruption detection and repair across all
repetitions.  The guarantee is deterministic: checkpointing, scanning,
and restoring are structural operations on the genome datastructure,
independent of model quality or prompt content.  This is the strongest
form of structural guarantee---verifiable, reproducible, and
unconditional.

\paragraph{Behavioral layer (statistical).}
The immune system's two-signal activation detects behavioral anomalies
with zero false positives (FP = 0\%) but limited sensitivity in
single-prompt evaluation (TP = 20\%).  The architecture is correctly
conservative: requiring both Signal~1 (baseline violation) and
Signal~2 (canary failure or repeated anomaly) prevents false alarms
at the cost of delayed detection.  This is appropriate for sustained
agent monitoring where observations accumulate over time, less so for
one-shot prompt classification.  The VerifierComponent extends this
layer with rubric-based quality evaluation---analogous to \emph{adaptive
immunity} (B-cell antigen recognition) complementing the innate
detection of ImmuneSystem.  When quality falls below threshold on a
fast model, the watcher escalates to the deep model, providing a
quality-sensitive escalation path that the novelty-based epiplexity
signal cannot.

\paragraph{Output layer (stochastic).}
The epiplexity monitor detects output \emph{repetition}, not output
\emph{mediocrity}.  A weaker model (Phi-3 Mini, quality 0.63) does
not trigger escalation because it produces varied output---the
stagnation signal measures novelty, not quality.  The wrapper tax
(+1{,}300 tokens, +27s) is the cost floor for structural wrapping,
justified for state integrity but not yet for output quality
improvement with capable models.

\paragraph{Developmental layer (preventive).}
The CertificateGateComponent implements the G1/S DNA damage checkpoint
analogy: before each stage executes its LLM call, the gate scans the
genome against a DNARepair checkpoint.  If corruption is detected, a
HALT intervention prevents the corrupted state from reaching the model.
Unlike the state layer's reactive repair (detect corruption after
execution, then restore), this is a \emph{preventive} gate---corruption
is blocked before it can affect output.  The guarantee is deterministic:
the gate checks structural integrity of the genome datastructure, not
model output quality.  This completes the cell cycle analogy alongside
CellCycleController, with CertificateGate enforcing the G1/S transition
that guards against propagation of damaged state.

The pattern mirrors the mechanism benchmarks: structural guarantees
that enforce invariants (state checksums, signal thresholds, integrity
gates) deliver unconditionally, while guarantees that depend on
information quality (embedding novelty, behavioral baselines) deliver
conditionally.
