research-results-auditor

Audit ML/statistics experiment outputs for validity, confounds, statistical support, calibration, and mismatch between measured results and claimed conclusions. Use when asked to interpret results, sanity-check benchmarks, review ablations, assess robustness claims, decide whether an experiment supports a paper claim, or produce a machine-readable result-au…

Independent GitHub snapshot · Automated classification · Original documentation governs