Position: State-of-the-Art Claims Require State-of-the-Art Evidence
State-of-the-Art (SOTA) claims pervade Artificial Intelligence (AI) and Machine Learning (ML) research. These claims rest on benchmark evaluations, where models are ranked by aggregate scores across tasks. Public benchmarks or leaderboards are the most visible instance, but the same structure appears in paper tables throughout the literature. However, such minimal evidence often cannot support these strong claims. We identify a widespread claim-evidence gap in AI benchmarking. Claiming SOTA carries implicit assumptions beyond mean score superiority, suggesting that a model meaningfully outperforms alternatives across most tasks. However, a marginal improvement in the mean score merely ind...
| Authors | YongKyung Oh |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 3 June 2026 |
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