Success criteria come from the claims themselves — nothing is back-filled after the fact.
claims.json · Grokking phase transitions in learning local rules with gradient descent
[claim] C1 — Critical exponent in 1D exponential model[claim] C2 — Critical exponent in D-dimensional uniform ball model[claim] C3 — Grokking probability increases with L1 regularisation[claim] C4 — Grokking probability decreases with dimension D
The auditor writes a numpy script per claim, runs it, iterates at most 3×. A positive control is mandatory on every run.
audit_summary.json · C1
[run] python audit_c1.py · positive control first[metric] nu_fitted = 0.9983[metric] nu_control = 1.000[metric] control_pass = true[metric] t_eps = 1.114[metric] t_train_zero = 1.102[control] control_pass: true — the statistic isn't lying
Run outputs, figures, failed attempts and reviewer references assemble into a Trackio logbook — the trail is the product.
Trackio logbook · evidence trail
[cell] 6 claim results → logbook cells[cell] 20 failed attempts preserved verbatim[cell] 10 evidence figures published[cell] literature-context cross-checks attached (Paperclip)
An automated judge checks structure, evidence, integrity, cost disclosure and trace completeness. The verdict is bound to the trail, not the outcome.
judge_report.json · PASS 5/5
[pass] structure — pass · claims.json (6 claims), audit_report.json present[pass] evidence — pass · 6 audited claims; issues: none[pass] integrity — pass · 0 claims skipped as untestable, all declared untestable at extraction[pass] cost disclosure — pass · per-claim attempt counts recorded; wall time in run files + trace[pass] trace — pass · 353 events, 31 LLM calls, 199 tool runs, 9 failed attempts preserved