Updated static source audit

• Data-dependent feedback. The same test inputs determine episode flags and severity and are reused for later assessments. The fresh-population certificate cannot be assumed for those later states.

• Activation diagnostic. The diagnostic uses assigned gain rather than effective gain and omits protection offsets and stress. At the default protection-zero kindling state it is the clean, unchallenged hidden diagnostic, not the biased-noisy one. Unequal batches are weighted equally rather than by example count.

• Longitudinal reset still has the exact identity. After the source reset, $g=1,a=1,\phi =\mathrm{ReLU}$, hence $g_e=1$ for every remaining protection level. Protection is still present through bias damping and offsets. The configured fifty decay calls still give, in exact real arithmetic, protection values $0.90,0.25,0.60$, independent of maintenance duration. Duration arms remain separately retrained branches. Scar state is not passed to the duration-arm clone CheungPsy03B.

• Autonomy wording remains unsupported. The final condition retains bias $0.3$, hidden noise standard deviation one, and assigned modulation. Its threshold is $0.70$, unlike the controller's $0.60$. It is weaker-challenge failure, not an untriggered clinical event.

• Persistent structural-interface defects. Quantile and kth-value ties do not enforce exact counts. A zero threshold can reactivate unscarred inactive mask positions. Zero regrowth still forces at least one coordinate per eligible tensor. The allocation flag is not implemented globally. Initial scarring still samples its fraction locally in NumPy but positions through global Torch randomness, and its strict availability inequality mishandles equality.

• Gradient scoring and state restoration. The first scoring backward pass does not clear old gradients. Restoration lacks a finally block. Scores are unsigned accumulated ambient derivatives, not the signed update of Psy03-A's local-descent theorem CheungPsy03A. A tensor checkpoint omits ordinary Python controls and external manager state.

• Some earlier configuration defects are now repaired. The adaptation sweep does use all four configured learning rates. The entry point runs the configured three iso-displacement seeds when the main seed count is at least three. These must not be reported as unused controls.

• Actual candidate counts. Each iso-displacement seed has

candidate configurations. The three-seed run has $393$ candidate executions. Candidates within one seed share a baseline and are not independent training-seed replicates.

• Matching remains unsupported outside tolerance. The matching function accepts but does not enforce tolerance. The configured absolute targets are replaced by seed-specific generated targets. A pooled-percentile fallback does not establish common finite-grid support.