Updated static source audit

The following findings concern the supplied complete 068g_cognitive_impact_v2.py, not an executed reproduction Cheung068G.

• Architecture and noise changed. Ordinary stress is now input-channel noise. Biased stress is hidden-update noise before external activation. The statement that all other logic is unchanged does not preserve the earlier forward model.

• Hidden reset and static repetition. Every forward call resets hidden state to zero. Datasets repeat one point over twenty steps. This supplies within-call recurrence, not hidden-state persistence across longitudinal calls or a delayed-cue task.

• Parameter accounting. Equation (eq:counts) gives the exact architecture count, not only an estimate. The three pruned tensors are gru_cell.weight_ih, gru_cell.weight_hh, and fc.weight. Pruning each packed GRU tensor does not enforce equal retention counts in its three gate blocks. Biases and the update-gate carry path remain.

• Scale interpretation. Gain acts on inputs and inhibition acts after the cell. Their product is not a common hidden-state multiplier. The newly recorded gain and inhibition changes are useful accounting, but the primary matching metric still uses weight-only L1 displacement.

• Ordinary and biased probe arguments. In evaluate_probe, a positive bias selects forward_with_biased_noise with the configured mania_test_sigma. That branch ignores the supplied internal_stress. Zero or negative bias selects ordinary forward, so a requested negative hidden bias is not implemented there. A repaired interface should select mode explicitly.

• Rotation convention. Right multiplication of row vectors implements the opposite signed angle from the printed column-vector convention. The transformation retains the original labels.

• Blend semantics. Voice labels are unused. Distractor coordinates are sampled with replacement from a finite empirical voice bank. The ideal fresh-uniform-center reference therefore needs source-law alignment before empirical use.

• Probe omission and hard-coded labels. The generated standalone voice loader is not evaluated by the battery. Keys such as rotation_45 and scaled_1.5 remain fixed even if configuration values are changed. Observation metadata must retain actual parameter values.

• Per-cycle stage distinction. The triggering biased accuracy and severity diagnostic are pre-scar. The cognitive battery and attached scar fraction are post-scar. These are intentionally different stages and must be labeled as such.

• Evaluation consumes future training randomness. Noise, blend indices, construction, shuffled loaders, scoring, and regrowth share global Torch randomness. Adding the new battery can therefore change later training and threshold-controller trajectories. Psy03-A's evaluation-isolation hypothesis is not satisfied CheungPsy03A.