Updated static source audit

• Aggregation can mix different targets and level counts. The same level_i can refer to different absolute targets across seeds. Overlap branches create five levels; fallback branches create four. Aggregation uses the first seed's level count, so an additional level from another seed can be omitted. A common absolute target or an explicitly defined seed-relative policy is required.

• Iso sweeps omit cognitive and longitudinal batteries. They evaluate acute stress and secondary pruning, not the cognitive battery, kindling cycles, or duration arms. The script does not establish matched-budget cognitive or longitudinal comparisons.

• Density stages and challenge differences remain. Sweep dose and acute outcomes are pre-challenge; stored sweep sparsity is post-challenge. The main quantile target and sweep active-fraction challenge are different integer procedures.

• Evaluation mode and dtype. Evaluators change train/eval state without always restoring it. Ordinary evaluation restores stress to zero rather than its incoming value. Hidden zero tensors use default dtype rather than x.new_zeros; mixed-precision or changed-dtype use requires explicit consistency.

• Automatic device selection. The source selects CUDA when available. Model construction, numerical precision, device-specific streams, deterministic settings, and software versions belong in provenance. A seed is not a cross-platform bitwise guarantee PyTorchRandom.

• Output interpretation. Unconditional cognitive, clinical, safety, and cost-effectiveness prose is not generated from the required certificates. Pooled candidate standard deviations describe heterogeneity, not independent-seed confidence intervals. The off-modulation iso table header is printed without aggregating and reporting the corresponding stored sweep outcomes.