State-of-the-Art Claims Require State-of-the-Art Evidence

In three linesCritical study of state-of-the-art claims in AI/ML. Analysis of 10 public benchmarks reveals that over 50% of top-model comparisons fail to support implicit superiority properties (meaningful effect size, cross-task consistency, robustness). Aggregate gains often driven by outlier datasets. Proposes more honest claim language without additional experiments.

Summary generated by Claude — human-verified