\section{Results}\label{sec:results}

All results are aggregated across 10 seeds with $N = 1{,}000$ trials
per seed per scenario.  Wilson 95\% confidence intervals are reported
where applicable.  Significance ($p < 0.001$) is determined by
non-overlapping Wilson CIs between biological and naive variants.

\subsection{Metabolism: Clear Win for Priority Gating}

Table~\ref{tab:metabolism} summarizes the metabolism benchmark results.

\begin{table}[h]
\caption{Metabolism benchmark: Biological (ATP\_Store + MTORScaler) vs Ablated (ATP\_Store only) vs Naive (flat counter). N = total operations or pressure events across 10 seeds.}
\label{tab:metabolism}
\centering
\small
\begin{tabular}{llccccc}
\toprule
Scenario & Metric & Bio & Abl & Naive & $\Delta$ & N \\
\midrule
bursty & critical served & 1.000 & 1.000 & 0.398 & \textbf{+0.602} & 24{,}139 \\
bursty & ops completed & 0.719 & 0.715 & 0.685 & \textbf{+0.034} & 2{,}000{,}000 \\
gradual depl. & critical served & 1.000 & 1.000 & 0.769 & \textbf{+0.231} & 43{,}372 \\
gradual depl. & ops completed & 0.979 & 0.996 & 0.950 & \textbf{+0.029} & 2{,}000{,}000 \\
constant & ops completed & 1.000 & 1.000 & 1.000 & 0.000 & 2{,}000{,}000 \\
sudden spike & ops completed & 1.000 & 1.000 & 1.000 & 0.000 & 2{,}000{,}000 \\
mixed priority & ops completed & 1.000 & 1.000 & 1.000 & 0.000 & 2{,}000{,}000 \\
\bottomrule
\end{tabular}
\end{table}

The headline result is \texttt{critical\_served\_under\_pressure}.  Under
bursty load, the biological system served 100\% of critical operations
($\text{priority} \geq 5$) during resource pressure, while the naive
flat counter served only 39.8\% ($\Delta = +0.602$, significant).  Under
gradual depletion, the gap is smaller but still significant: 100\% vs
76.9\% ($\Delta = +0.231$).

Against the naive baseline, the biological system actually \emph{improves}
total throughput: 71.9\% versus 68.5\% under bursty load ($\Delta = +0.034$),
because priority gating preserves budget for later operations that the
flat counter would miss entirely.  Comparing biological against ablated,
both achieve identical critical-service rates (100\%).  However, under
gradual depletion the mTOR scaler's anticipatory conservation gates some
non-critical operations earlier: biological completed 97.9\% versus
ablated's 99.6\%.  This 1.7 percentage-point throughput reduction is the
cost of the mTOR scaler's preemptive feature gating, which in these
experiments did not produce an additional critical-service benefit
beyond what ATP\_Store's own state machine already provides.

Under constant load, sudden spike, and mixed priority, all three
variants perform identically.  The biological complexity only matters
under specific pressure patterns where resource contention forces
choices about what to serve.

\subsection{Quorum Sensing: Unique Precision--Recall Operating Point}

Table~\ref{tab:quorum} summarizes key quorum sensing results.

\begin{table}[h]
\caption{Quorum sensing benchmark: selected configurations. Bio = QuorumSensingBio (signal accumulation + decay), Abl = IndependentActors (no coordination), Naive = MajorityVote.}
\label{tab:quorum}
\centering
\small
\begin{tabular}{llccccc}
\toprule
Config & Metric & Bio & Abl & Naive & $\Delta$ & N \\
\midrule
static, $n$=20, $c$=8 & TPR & 0.866 & 1.000 & 0.005 & \textbf{+0.861} & 300{,}000 \\
static, $n$=10, $c$=4 & TPR & 0.711 & 1.000 & 0.052 & \textbf{+0.659} & 300{,}000 \\
static, $n$=20, $c$=4 & TPR & 0.748 & 1.000 & 0.000 & \textbf{+0.748} & 300{,}000 \\
gradual, $n$=20, $c$=8 & TPR & 0.718 & 0.999 & 0.002 & \textbf{+0.716} & 216{,}959 \\
\midrule
all $c$=0 configs & FPR & 0.000 & 0.048--0.180 & 0.000 & 0.000 & 300K ea. \\
\bottomrule
\end{tabular}
\end{table}

The three coordination strategies occupy distinct positions on the
precision--recall curve:

\begin{itemize}[nosep]
  \item \textbf{Independent actors} (ablated): 100\% TPR but 5--18\% FPR.
        Catches everything but cries wolf on 1 in 6--20 clean time steps.
  \item \textbf{Majority vote} (naive): 0\% FPR but near-0\% TPR.
        Almost never detects threats because clean agents ($\mu = 0.15$)
        always outvote compromised agents at the $0.5$ threshold.
  \item \textbf{Signal accumulation} (biological): 0\% FPR with
        71--87\% TPR at 40\% compromise.  Zero false alarms with
        meaningful detection.
\end{itemize}

The biological model's advantage comes from two structural properties:
(1)~\emph{continuous signals} average out noise (unlike binary votes),
and (2)~\emph{temporal decay} ensures stale evidence ages out (unlike
a vote that persists forever).  The combination naturally filters noise
while remaining sensitive to sustained patterns.

Detection sensitivity scales with both the number of agents and the
compromise fraction, consistent with the biological signal-accumulation
model: more agents producing more signal yield higher accumulated
concentrations relative to the threshold.

\subsection{Epiplexity: Embedding Quality Determines Outcome}

The epiplexity benchmark produced the most instructive result: the
biological design's performance depends critically on embedding quality.
With mock embeddings (SHA-256 hash-based pseudo-embeddings), the naive
detector wins.  With real semantic embeddings (all-MiniLM-L6-v2), the
biological design dominates on convergence discrimination---the exact
capability the two-signal design was built for.

Table~\ref{tab:epiplexity} shows results with both embedding providers.

\begin{table}[h]
\caption{Epiplexity benchmark with mock vs real embeddings.  Bio =
EpiplexityMonitor (two-signal Bayesian), Naive = cosine similarity +
timeout.  Real embeddings: all-MiniLM-L6-v2 ($N = 100$ trials, seed
42); mock embeddings: SHA-256 hash ($N = 1{,}000 \times 10$ seeds).}
\label{tab:epiplexity}
\centering
\small
\begin{tabular}{lll ccc}
\toprule
Embedding & Scenario & Metric & Bio & Naive & $\Delta$ \\
\midrule
\multirow{4}{*}{Mock}
  & loop & accuracy & 0.467 & \textbf{0.940} & $-$0.473 \\
  & convergence & accuracy & 0.473 & \textbf{1.000} & $-$0.527 \\
  & false stagn. & accuracy & 0.540 & \textbf{0.993} & $-$0.453 \\
  & false stagn. & FP rate & 0.052 & \textbf{0.007} & $+$0.045 \\
\midrule
\multirow{5}{*}{Real}
  & convergence & accuracy & \textbf{0.960} & 0.401 & $+$0.559 \\
  & false stagn. & accuracy & \textbf{0.960} & 0.020 & $+$0.940 \\
  & false stagn. & FP rate & \textbf{0.000} & 0.980 & $-$0.980 \\
  & loop & accuracy & 0.571 & \textbf{0.940} & $-$0.369 \\
  & loop & FP rate & \textbf{0.000} & 0.750 & $-$0.750 \\
\bottomrule
\end{tabular}
\end{table}

\paragraph{With mock embeddings: naive wins.}
The naive cosine-similarity detector outperforms the biological design
on detection accuracy across all scenarios.  However, the
false-positive/false-negative breakdown reveals a precision--recall
tradeoff: the biological variant achieves 0\% FP rate on loops (versus
naive's 75\% FP), at the cost of 58\% FN rate.  With mock embeddings,
the novelty signal is essentially random noise---semantically similar
messages receive unrelated cosine similarity---so the two-signal
combination performs worse than a single signal.

\paragraph{With real embeddings: biological wins on convergence.}
Switching to all-MiniLM-L6-v2 sentence embeddings flips the result
on convergence discrimination.  The biological monitor achieves
96.0\% accuracy on both convergence and false-stagnation scenarios,
versus 40.1\% and 2.0\% for the naive detector.  The naive detector's
false-positive rate on false stagnation reaches 98.0\%---it sees
similar outputs and screams ``stagnant'' regardless of the agent's
confidence level.

The biological design's two-signal structure now earns its complexity:
the perplexity signal distinguishes convergence (low novelty + low
perplexity = healthy) from stagnation (low novelty + high perplexity =
pathological).  The naive cosine detector has no access to this
distinction.

\paragraph{Loop detection still favors naive.}
Even with real embeddings, the naive detector wins on loop detection
(94.0\% vs 57.1\%) because exact repetition is trivially detectable by
cosine similarity.  The biological monitor's advantage is not on easy
cases (identical outputs) but on hard cases (semantically similar but
subtly different outputs where the agent's confidence level matters).
