\section{Limitations}\label{sec:limitations}

\paragraph{Embedding dependence.}
The epiplexity benchmark was run with both mock embeddings (SHA-256
hash-based) and real sentence embeddings (all-MiniLM-L6-v2).  The
results differ dramatically: the biological design loses with mock
embeddings and wins with real embeddings on convergence discrimination.
The real-embedding results use a single model and $N = 100$ trials
(versus $N = 10{,}000$ for mock embeddings); confirmation with
additional embedding models and larger sample sizes would strengthen
the finding.

\paragraph{Synthetic load patterns.}
The metabolism benchmark uses programmatically generated operation
sequences (constant, bursty, gradual, spike).  Real agent workloads
may have different statistical properties---heavier tails, correlated
bursts, priority distributions that differ from our 30\% critical
assumption.

\paragraph{Simulated agents.}
The quorum sensing benchmark simulates agent suspicion signals as
Gaussian draws around known baselines.  Real multi-agent systems have
richer signal environments: suspicion may be autocorrelated, agents
may have heterogeneous detection capabilities, and compromised agents
may adapt their behavior to avoid detection.

\paragraph{Limited end-to-end evaluation.}
Section~\ref{sec:e2e-eval} provides initial end-to-end results
primarily with Gemma~4 27B (plus a limited Phi-3 Mini comparison on
stagnation) and self-judging across 3~repetitions.
The positive result (state integrity) is deterministic and
model-independent.  The statistical results (20\% injection TP, 0\%
stagnation escalation) are honest but underpowered: larger sample
sizes, cross-model validation, and external judging would strengthen
the findings.  The immune system's behavioral detection is evaluated
in single-prompt mode, which underestimates its design intent as a
sustained monitoring system.

\paragraph{Manual thresholds.}
Several parameters are manually set rather than auto-calibrated:
the mTOR hysteresis margin ($h = 0.05$), quorum sensing decay
half-life ($5.0$ time units), threshold base ($10.0$), and
epiplexity mixing parameter ($\alpha = 0.5$).  Sensitivity analysis
across these parameters is not provided.

\paragraph{Statistical assumptions.}
Wilson CIs assume independence across trials within seeds.  The
10-seed aggregation partially addresses between-seed variability,
but within-seed trials share the same random number generator state
progression.
