This package has two distinct results. Neither refutes JMLR Theorem 17.
- Matched available-budget study: 18 synthetic regimes, five seeds each, 270 selected models. Released ASkotch has lower mean test MSE in 13 of 18 regimes; the inducing comparator wins the other five. The experiment gives every method 12 candidates and a four-second cap per candidate. It does not equalize consumed time: inducing PCG often stops early. See the corrected primary report.
- Exact rank-100 mechanism: at the released recommended block fraction and rank, the actual ten-step randomized power estimate yields a mean-square instability in an explicitly highly ill-conditioned RBF family. Theorem and assumptions. A fixed relative step of one has an exact expected-error repair on the same family. Repair.
The retained finite float64 runs all converged. Expectation instability does not imply every realization fails. The runs use the unchanged optimizer with a structured kernel adapter, rather than the untouched full large KeOps pipeline. Float32 exceptions and all finite checkpoints are retained. Finite-run report. The 140-case moderate-conditioning search did not find a case meeting its instability threshold; this negative result is preserved in its report.
From the repository root, with Python 3.11+ and NumPy installed:
python kernel/verify.pyThis independently checks the rational covariance/certificate algebra, saved fixed-step certificate, all 72 retained finite states, all 54 paired regime comparisons, and the complete negative moderate-conditioning grid. It works on temporary copies, preserving released artifacts. It does not launch a benchmark, search for a failing seed, or contact a cloud service.
Original benchmark configuration and driver are in regime_replicates/.
They require the pinned upstream implementation and its GPU environment;
the verification command above recomputes reported arithmetic, not timings.
The upstream implementation is fast_krr at commit
61cdb69.
Primary paper: Have ASkotch.
The source-bound analytic reviews are internal AI-assisted checks, not
external expert review, formal verification, or novelty certification.
PUBLIC_PROVENANCE.json records original and public artifact hashes, including
explicit removal of local machine paths and the corresponding protocol-hash
update. Scientific measurements and proof equations are unchanged. Historical source
snapshots mentioned in older notes are not a vendored copy of upstream code.
