[Parity #478] Complete learned-model-state diagnostics for every canonical estimator family

[godofecht]

Parent: #478

Performance must not outrun semantic evidence. Current benchmark infrastructure has strong state diagnostics for only a subset of rows (notably PCA/KMeans and selected later additions); score parity alone is insufficient for optimizer work.

Add estimator-state comparisons for every canonical family: linear coefficients/intercepts and objective/convergence state; SVM weights/support vectors/dual coefficients/intercepts; tree node structure/thresholds/features/leaves; forest per-tree/bootstrap/RNG structure; GaussianNB priors/means/variances; KMeans centers/inertia/iterations; PCA means/components/singular values; Ridge/Lasso/LinearRegression coefficients/intercepts/objective state; KernelRidge dual coefficients/kernel parameters.

Acceptance: all 19 canonical rows report state_coverage=covered or an explicit semantically justified exception; state deltas are frozen in disparity history; performance classification is blocked when required state evidence regresses beyond contract.

[godofecht]

I worked through a semantic hole in learned-state parity: raw coordinate comparison is not valid for estimator families with exact state symmetries. KMeans centers must be compared modulo cluster-label permutation; PCA retained state should include an invariant subspace/projector comparison because sign flips and, under repeated/degenerate singular values, orthogonal rotations of the retained basis can represent the same model. A general contract is orbit distance d_G(theta,theta') = inf_g d(theta,g theta') for the estimator's semantics-preserving symmetry group. Concrete counterexamples: three KMeans centers can have raw max error 10 yet permutation-aware error 0; two PCA component matrices can have raw max error 1 yet identical V^T V projectors. I added the full derivation and an idiomatic Flow witness to godofecht/OpenProjects under ml/symmetry-aware-learned-state-parity.{md,flow}. Recommendation for #492: keep model_state_coverage.py as inventory, but require estimator-specific semantic-state schemas: KMeans minimum-cost center matching before state deltas; PCA mean+spectrum+projector parity, with sign-aligned row comparison only for nondegenerate components. Performance eligibility should depend on those semantic certificates, not merely the presence of any diagnostic key.