Source-pattern audit and debugging decisions

This ledger retains the supplied development brief's findings DevelopmentBrief. The public task definitions were separately aligned with 069av2.py CheungPublicCode. It is not a claim that every version underlying every published table has been independently reconstructed.

• Notebook syntax versus a Python script. The public export contains a leading notebook shell command. Remove it from a standard Python source and declare dependencies separately. A parse failure occurs before an experiment can run.

• Missing rule evidence. Under the stated point-only assessment interface, neither rule IDs, past targets, correctness, nor rewards enter the recurrent update. Theorem thm:observability supplies the exact consequence.

• Duplicated label mappings. Use the distinction between IDs, effective functions, and informative disagreements. Do not describe four IDs as four independent rules.

• Training and assessment schedules. Random training switches and deterministic assessment switches are different schedule laws. They do not remove the uncued observation obstruction when both remain independent of the points and preserve uniform rule marginals.

• Response-repetition naming. The reported post-switch error metric repeats the preceding response, not the old rule's prediction on the current point. Retain it under the neutral name in (eq:repeatmetric).

• Stable-window coverage. Construct stable windows from all segments, including the final segment. Report empty windows rather than dropping them without a selection record.

• Window overlap. Stop a post-switch window at the next switch. Otherwise a statistic attributed to one switch can include responses under another rule. The corrected metric definition changes that protocol.

• Recovery bounds and censoring. State whether time refers to a window start or completion. Do not search through later switches without declaring that estimand. A sentinel is not a censoring indicator. Include the final permitted window start when implementing an inclusive observation horizon.

• Rule-inference metric. Classification accuracy on selected stable windows is still classification accuracy. It is not a direct estimate of the hidden rule-ID posterior.

• Entropy scope. Marginal output-label entropy and temporal conditional entropy are different. A high marginal entropy can coexist with deterministic repetition patterns.

• Mixed gradient interfaces. In-forward masked linears have ${\nabla }_{W}L=M\circ {\nabla }_{B}L$. Post-step-zeroed recurrent parameters can have nonzero ambient inactive derivatives. Record which interface supplied each score.

• Restoration reference. A manager built after loading zeroed weights does not recover an immutable pre-pruning reference. Mask access can reopen while the numerical initialization remains zero. Preserve reference weights separately.