Question, contribution, and evidence boundary

The central question is:

When does a repeated threshold-triggered damage-and-repair process produce persistent vulnerability, and which apparent effects are consequences of imposed update rules rather than independently demonstrated sensitization?

Cheung's pruning and computational intervention studies provide the immediate motivation CheungPruning,CheungModes,CheungCliff. His separate study of iterative language-model adaptation provides additional motivation for distinguishing structural interventions from changes in susceptibility under weaker challenges CheungLLMKindling. That study uses different tasks and experimental objects. It does not validate the permanent-mask rule studied here.

Psy01-A separates structural counts, weight geometry, margins, noise, and optimization schedules CheungPsy01A. Psy03-A treats individual interventions and their comparison budgets CheungPsy03A. This successor adds longitudinal feedback and persistent structural state rather than another asserted treatment ranking.

Psy02-A and Psy02-B motivate the separation of quantum channels, accessible observations, complete records, and mechanism identification CheungPsy02A,CheungPsy02B. Their results are cited as source records and methodological precedents. The new objects, hypotheses, and independent proofs are printed below.

implementation source. The object audited is the complete supplied file 068e_kindling_v2.py Cheung068E. The audit is static. No mutable repository revision is identified with this snapshot, and no numerical table is independently reproduced here.

contributions. The paper supplies:

1. irreversible-mask invariants and repair-capacity limits;

2. feedback-aware threshold certification;

3. counterfactual definitions and certificates for longitudinal sensitization;

4. a quantum stochastic network with an explicit persistent classical structural-memory register.

and clinical boundary. In theorem statements, a “scar” is a permanently forbidden parameter coordinate or graph edge. An “episode” is a model-defined controller event. We use biased-noise error, threshold crossing, permanent deletion, and low-challenge failure.

The source's “mania,” “relapse,” “kindling,” and drug-class labels are computational analogies. Classification error under an imposed perturbation is not a validated clinical probability. Neither a threshold nor a percentage supplies that validation.

and verification. Coordinate projections, Lipschitz propagation, concentration, and channel contraction are standard. The contribution is their longitudinal assembly, source-specific identities and repairs, and the explicit memory-bearing pilot. No theorem proves convergence of the source's Adam training. Compilation, software execution, formal proof checking, and biological validation are not represented as completed.