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The Hebrew University of Jerusalem
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Systematic gaps in AI evaluation reporting are exposed, revealing inconsistencies that hinder reliable comparisons across thousands of models and benchmarks.
Standard LLM benchmarks miss the mark: personalized "vibe-testing" reveals that user-specific prompts and subjective criteria can flip model rankings.
