Source-pattern audit and debugging decisions

• Activity versus loss influence. Accumulated absolute loss derivatives are loss-influence scores, not direct measurements of synaptic activity. ReLU saturation or an unused branch can give zero scores for reasons unrelated to biological inactivity.

• Protective-score direction. A per-tensor positive multiplier cancels. Global division lowers designated scores. Theorems thm:cancel and prop:division identify the two different issues.

• Unused control and topology. A configured striatal-specific multiplier has no effect if never read. A pointwise striatal readout is not a second recurrent loop unless it is fed back or has its own state.

• Dropout and evaluation mode. Training-mode interlayer dropout and evaluation-mode computation are different laws. PyTorch locates GRU dropout between layers PyTorchGRU. Mode restoration and the assessed mode belong in provenance.

• No permanent scar register. An active mask and regrowth eligibility are not Psy03-B's separate permanently forbidden coordinate set. Its scar-capacity theorem is not silently transferred.

• Composite challenges. Cumulative pruning with rising stress changes two controls. An evaluation after stress reset is a different endpoint from the stressed trajectory. Report both if both are claimed.

• Matching statuses. Interpolated outcomes are surrogate estimates. Nearest achieved candidates must retain residual mismatches. Pooled target support is not common arm support.

• Isoacute implementation. A notebook heading containing “isoacute” does not establish calibrated acute matching and independent durability evaluation. Theorem thm:isoacute states the needed procedure.

• Candidate-minimum summaries. Independent seed counts do not turn different selected candidate minima into one executed matched intervention. Retain candidate IDs, absolute targets, and selection rules.

• Architecture ablations. GRU and LSTM use different gate counts and parameter spaces. A modular-off toggle can leave unused branch parameters registered; then total stored count need not decrease although active computation changes. Report registered parameters, used parameters, masks, and computational cost separately.

• Random-stream isolation. Training, scoring, initialization, dropout, assessment, and selection need named streams and complete replay state. A seed is not a proof of statistical independence or cross-platform bitwise identity PyTorchRandom.

• Endpoint interpretation. A validated computational effect is not clinical efficacy, OCD mechanism identification, or a treatment recommendation. Printed interpretations must remain within the certified task and intervention scope.