Ten runtime metadata items that large model evaluation reports should include

The article proposes that large model evaluation reports should record ten runtime metadata items, including model version snapshots, dataset hashes, decoding parameters, criteria versions, tool degradation, user simulator versions, hardware and region, time windows, sample size and confidence information, and the storage location of failed samples. The author believes that two scores can be directly compared only when the model snapshot, dataset identifier, and decoding parameters are consistent, and provides a compare_gate.py example that blocks two reports from the comparison table when conditions are missing or inconsistent. The numerical values in the article are only used to illustrate field formats and do not correspond to any real model results.