icml-reproducibility — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited icml-reproducibility (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this when the paper's acceptance risk is tied to whether experiments, code, or theory can be trusted. ICML reviewers are asked to evaluate soundness, and ICML author instructions state that reproducibility and code availability are considered in decisions.
Accepted papers may publish original supplementary material. Do not put unreleasable data, identity leaks, or private credentials in the review package. If data cannot be public, document the access path and ethics constraints in the paper.
ICML reviewers fold reproducibility into the soundness judgment rather than scoring it on a separate axis, so the question is whether a skeptical reviewer could regenerate the headline number.
| Signal a reviewer checks | Strong evidence | Weak signal that invites doubt |
|---|---|---|
| Code in review package | Anonymized, runnable, exact commands | "Code on acceptance" promise only |
| Variance | Seeds with intervals | Single run, no spread |
| Compute | Hardware and budget table | Unstated cost, unfair comparison |
| Theory | Assumptions and proof dependencies listed | Theorem with hidden conditions |
For an adaptive-optimizer paper, reproducibility means the convergence proof's assumptions are written out, the benchmark scripts run from the anonymized supplement with a fixed seed, and the compute table lets a reviewer judge whether the speedup is real or a tuning artifact. The recurring failure is a clean theorem paired with benchmark code that silently relies on an unreleased internal dataset; document the access path or move to a public dataset before the deadline.
| Pushback | ICML-specific fix |
|---|---|
| "Cannot reproduce without the code" | Ship the anonymized runnable package now, not a post-acceptance promise |
| "Hyperparameters undocumented" | Add the search protocol and final values to the appendix |
| "Speedup may be a seed artifact" | Report multiple seeds with confidence intervals |
| "Theorem assumptions hidden" | List assumptions, proof dependencies, and edge cases explicitly |
Because accepted ICML papers can publish the original supplementary material on OpenReview, the reproducibility package is also a public commitment. Confirm before the deadline that every file is releasable, every license is stated, and no private credential or identity path remains.
[Reproducibility status] strong / adequate / weak
[Weakest claim] <claim not yet supported>
[Required fix] <code/data/seed/compute/baseline/proof>
[Supplement/public-record risk] <none or issue>
[Reviewer-facing sentence] <concise reproducibility statement>~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.