Five loops ran the DGM procedure with every diagnosis replaced by a request drawn from a clean list, and three DGM loops
ran at the same time. The clean list is the second-round list with the three failure-related requests described in
\S\ref{sec:round2} replaced by three generic ones. The one aimed at empty responses also
asked the agent to answer every tool call, which is part of move L. In the third-round batch, which also re-evaluated the earlier loops' agents and where the seed scores $\rcSeedF\%$, the five clean
random-diagnosis loops
gain $\rcClean$ points [$\rcCleanlo$, $\rcCleanhi$] on average, with individual loops from $\rcCleanMin$ to
$\rcCleanMax$. All nine DGM loops gain $\rcDgmAll$ [$\rcDgmAlllo$, $\rcDgmAllhi$], and the three that ran at the same
time $\rcDgmNew$ [$\rcDgmNewlo$, $\rcDgmNewhi$]. The registered non-inferiority test fails: the clean loops trail the DGM loops
by $\rcHfourteenAbs$ points [$\rcHfourteenAbslo$, $\rcHfourteenAbshi$], and at the level of loops the difference is
significant (Welch test, $p=\rcHfourteenP$). The registered secondary contrast, against the three same-time DGM loops,
is $\rcCleanNew$ [$\rcCleanNewlo$, $\rcCleanNewhi$] ($p=\rcCleanNewP$).

The remaining contrasts in this batch are exploratory. The random-diagnosis loops of the second round gain $\rcRandOld$
[$\rcRandOldlo$, $\rcRandOldhi$]. That is
$\rcCleanOldAbs$ points [$\rcCleanOldAbslo$, $\rcCleanOldAbshi$] more than the clean loops ($p=\rcCleanOldP$) and level
with all nine DGM loops ($\rcRandAll$ [$\rcRandAlllo$, $\rcRandAllhi$]). The same-time DGM loops reproduce the earlier
ones within $\rcNewOldAbs$ points. Search over generic requests therefore gains much less when no request points at the
failure, and the DGM's diagnosis, which reads the failure logs, does better. The single model-loop move L gains $\rcL$
[$\rcLlo$, $\rcLhi$] in this batch, more than the average DGM loop but $\rcLoopThreeLAbs$ points [$\rcLoopThreeLAbslo$,
$\rcLoopThreeLAbshi$] less than the agent of loop 3.