An auto agent enhances benchmarking by utilizing adaptive memory, which retains previous performance data to improve future evaluations. The effectiveness of this memory is underscored by the decline in benchmark quality when it is removed, indicating that it plays a critical role. Additionally, the use of a novelty metric uncovers unexpected performance disparities among well-known models on specific tasks, revealing that even top models like Gemini Pro may underperform on novel topics such as the Permian extinction compared to smaller models, emphasizing the importance of comprehensive benchmarking strategies that consider both novelty and historical performance.

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