Tier 0 — stochastic-method inventory (authoritative)

Derived from source: the set of C-kernel files that contain a file-local splitmix64 generator (grep -rln splitmix64 cpp/src/core/) is the true stochastic surface. Everything else is deterministic or a Python composite that bypasses the C kernel.

The 14 splitmix64-using core files

12 stochastic selectors (RNG-parity scope): cars_selection, emcuve_selection, ga_selection, iriv_selection, irf_selection, pso_selection, random_frog_selection, randomization_selection, scars_selection, stability_selection, uve_selection, vissa_selection.

2 infrastructure (also RNG, but not selector parity): model.cpp (randomized-SVD component sampling — context-seed + component offset), validation.cpp (CV fold assignment).

Dashboard parity policy (2026-05-30)

The dashboard now separates three selector outcomes instead of collapsing all feature-mask differences into a red failure:

• selection_exact → exact selector parity (Jaccard = 1.00).

• selection_divergence_allowed → cross_check with the recorded reason (for example t2_select, where the statistic matches but the loading-weight convention differs).

• documented selection_mismatch methods → cross_check with an amber BD J badge and a tooltip explaining why exact feature-mask parity is not expected.

Unmapped selection_mismatch rows still render as real divergent failures. This keeps the matrix strict by default while making the known RNG/noise/model exceptions explicit.

Post-investigation selector verdicts (2026-05-30)

method	canonical default	reference + its RNG	verdict
uve_select	R plsVarSel::mcuve_pls	R-MT	canonical dashboard path exact; legacy C++ path remains a documented BD J cross-check because its noise count, RNG, and folds differ
emcuve_select	R mcuve_pls ensemble	R-MT	exact in the current matrix
stability_select	R mcuve_pls stability analogue	R-MT	BD J cross-check — R mcuve stability and n4m legacy stability use different subsampling/std/RNG-noise conventions
cars_select	R enpls.fs analog	R-MT	BD J cross-check — executable reference is an enpls importance analog, not n4m CARS proper
spa_select	R plsVarSel::spa_pls	R-MT	BD J cross-check — R uses random subsampling + Wilcoxon/permutation logic; n4m legacy SPA is deterministic
ga_select	R GA selector	R-MT	cross_check — GA is highly RNG/impl-sensitive
iriv_select	NumPy IRIV port	numpy PCG64/default_rng	BD J cross-check — NumPy/scipy rank tests and n4m legacy candidate generation are not the same algorithmic path
random_frog_select	auswahl RandomFrog	numpy RandomState-MT	BD J cross-check — different MCMC proposal chain and random stream
vissa_select	auswahl VISSA	numpy RandomState-MT	BD J cross-check — different submodel sampling/aggregation path
irf_select	auswahl IntervalRandomFrog	numpy RandomState-MT	BD J cross-check — different interval proposal chain
randomization_select	R randomization (pvals<alpha)	R-MT	exact in the current dashboard through the canonical base-R adapter; native R-MT C++ path remains an optional future parity hardening
rep_select	R plsVarSel::rep	R-MT	BD J cross-check — repeated VIP Monte-Carlo selection vs bounded backward-elimination steps
shaving_select	R shaving	R-MT	BD J cross-check — CV-error survivor path vs explicit step-count trajectory
bve_select	R plsVarSel::bve	R-MT	BD J cross-check — R sampling/VIP elimination vs deterministic greedy CV-RMSE elimination
ipw_select	R plsVarSel::ipw	R-MT	BD J cross-check — R RC filter/package thresholds vs n4m top-k iterative scores
st_select	R plsVarSel::st	R-MT	BD J cross-check — relative shrink ladder vs absolute coefficient thresholds
pso_select	Python pyswarms BinaryPSO	NumPy/Python PSO RNG	BD J cross-check — different swarm updates, velocity handling, and RNG streams
scars_select	NumPy SCARS port	NumPy RNG	BD J cross-check — different subsampling, shrinkage, and random stream details
wvc_select	R WVC	deterministic/current R path	exact in the current matrix
wvc_threshold_select	R WVC threshold	deterministic/current R path	BD J cross-check — median-scaled R threshold vs n4m min-selected threshold convention
t2_select	R plsVarSel::T2_pls	loading-weight convention, not RNG	documented selection_divergence_allowed cross-check

Notes:

• The numpy/sklearn composites (bagging_pls, boosting_pls, random_subspace_pls, n_pls, pls_qda, pls_glm, pls_cox, pls_logistic, group_sparse_pls) BYPASS the C kernel — not_available for C-kernel bindings, never an RNG fix.

• iriv_select uses PCG64/default_rng in the NumPy reference path.

• The auswahl trio (random_frog, vissa, irf) uses numpy RandomState MT19937 through sklearn check_random_state.

RNG engines required (and status)

engine	reproduces	status
splitmix64	n4m’s own legacy streams	exists; legacy selector streams are intentionally preserved
PCG64	numpy.random.default_rng	DONE (rng_pcg64) — covers IRIV-style NumPy references
R-MT + Inversion	base R set.seed/runif/rnorm	DONE (rng_mt_r, bit-exact) — covers the R selectors
numpy RandomState-MT	legacy numpy.random.RandomState	DONE (rng_numpy_mt, bit-exact) — covers auswahl-style references

Completed rollout

1. Tier 0 inventory — done.

2. RNG engines — splitmix64 preservation plus PCG64, R-MT, and numpy-RandomState-MT engines are available behind the additive n4m_rng_kind_t ABI.

3. UVE pilot — R-exact MCUVE path is available through rng_kind=MT_R while the legacy splitmix path remains byte-compatible.

4. Dashboard contract — known RNG/noise/model selector mismatches are rendered as documented cross_check/BD J cells; unmapped mismatches remain red failures.

Further hardening would mean porting entire external selector algorithms into C++ (for example R bve_pls, R spa_pls, auswahl VISSA/RandomFrog). That is a method implementation project, not an RNG-engine swap.