Static audit of the supplied comparison pipeline

• Pre-existing gradients are not cleared before the first scoring backward pass. Fresh clones normally begin with no gradients, but repeated uses of the interface can add an old gradient to the first score. Clear gradients before every scoring pass and restore state in a finally block.

• Starting tensors do not specify complete state. Modulation attributes, masks, phase, optimizer conventions, and stream states must also be saved or reset explicitly.

• Global randomness links branches to their execution order. Initialization, shuffling, gradient sampling, regrowth, and evaluation consume shared randomness. Additional evaluations can change later checkpoints. This invalidates a claimed paired replay, though shared randomness alone is not a theorem of biased population expectations.

• Evaluation mutates the stress attribute. The source restores it to zero rather than restoring its incoming value. The two evaluation functions otherwise have the same computation; their difference is the retained model modulation.

• Printed clinical conclusions are unconditional prose. The treatment-selection and combination tables are not consequences of validated comparison statistics. They are excluded from the repaired evidence output.

• Durability and onset are not identified. Preset epoch counts are not biological times. One additional pruning challenge is not a longitudinal relapse model. Different training lengths and adaptation costs require explicit resource accounting.

The official software documentation also distinguishes reproducibility within a controlled environment from guarantees across releases and platforms PyTorchRandom. Stable sorting provides the declared tie rule PyTorchArgsort. No software-documentation statement is a biological certificate.