star-expt-analyst

Auditing experiment results involves verifying artifacts, log health, and performance metrics against defined plan criteria. This systematic research workflow provides reproducible validation and evidence-backed reporting without modifying source files.

Scanning logs for fatal signals like OOM, NaN, or divergence is done by monitoring log health without loading massive files into memory. This process identifies training instabilities and provides structured results aggregation for analysis.

Yes, you can compile verified results across multiple experiment runs into a structured table for paper-ready analysis. This cross-run aggregation compares performance metrics against defined done-criteria to ensure reproducible validation.

Evidence-based auditing in AI research automatically verifies metrics and deliverables against plan requirements by re-opening cited source files. It solves inconsistent experiment reporting by providing systematic, reproducible validation of training runs.

No, the experiment audit process does not modify source files. It performs read-only verification of artifacts and performance metrics, generating a persistent analysis report in the project directory instead of altering original data.