Static audit of 068e\ kindling\ v2.py

The following findings are established by inspection of the supplied file Cheung068E, not by executed reproduction.

• Unused kindling seed argument. simulate_kindling accepts seed but never uses it. The outer main experiment seeds global generators, so this does not mean the entire program lacks seeding. It means the routine has no isolated branch replay from its argument.

• Global-stream coupling. Model construction, shuffled loaders, scoring, regrowth, initial-scar placement, and evaluation use global Torch randomness. Adding an evaluation or changing branch order can change later training states. The one global seed does not isolate these roles.

• Controller data reuse. The same test inputs determine threshold updates and severity throughout the kindling loop. Its later checkpoint assessments are not independent held-out population assessments.

• Stage mixing. Cycle accuracy, activation magnitude, and gain are measured before scarring. The recorded scar fraction and sparsity are queried after scarring. The code computes pre-update structural quantities but does not store them as such.

• Gain reset and fixed residual protection. Proposition prop:reset applies exactly. The final effective gain is one in every duration arm, and the assigned protection remaining after fifty calls does not depend on maintenance duration.

• Longitudinal scar-state omission. simulate_longitudinal_manic_relapse receives no scar state and initializes all scar masks to zero. The active masks are copied, so its present support is retained. Because this particular routine performs no regrowth, the omission need not change its immediate forward results. It nevertheless fails to preserve complete permanent eligibility state for extension or replay. The kindling routine, in contrast, does restore scar masks.

• Diagnostic scope. The activation diagnostic ignores protection, offsets, challenge bias, and noise. At the default $p=0$ of kindling, omission of protection does not change its clean diagnostic. Calling it the actual perturbed activation remains incorrect.

• Imposed severity and nonuniform placement. Gain appears explicitly in the severity formula. Episode deletion selects smallest active magnitudes. A common base rate is not uniform random scarring.

• Ties and endpoints. Episode and secondary pruning use cutoff inequalities and can delete more than the requested count. Magnitude quantiles do not enforce exact retained counts. At sparsity one, the maximum-magnitude positions can remain.

• Mask reactivation and allocation. prune_by_magnitude reranks all unscarred positions, including inactive zeros, rather than restricting deletion to the current active mask. A zero threshold can reactivate inactive mask positions. The accepted per_layer flag does not supply a global allocation implementation.