\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{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.

However, the epistemic bounds currently lack empirical validation
against measured behavior.  The metabolism and quorum sensing
benchmarks now have stronger empirical backing than the topology
theorems.  Validating the predicted error amplification and sequential
overhead bounds against actual benchmark measurements remains future
work.

\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.
