Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Summary
This paper introduces EvalCards, an operational framework that standardizes AI evaluation reporting by composing benchmark metadata, evaluation run data, and model metadata into a unified record with interpretive signals for reproducibility, completeness, provenance, risk, and score comparability. The authors deploy a monitoring tool across thousands of models and benchmarks, revealing systematic gaps in current reporting practices.
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Source: https://huggingface.co/papers/2606.09809 Authors:
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Abstract
AI evaluation results suffer from inconsistent reporting across platforms, prompting the development of EvalCards, an operational framework that standardizes benchmark metadata, evaluation data, and model information into a unified, interpretable record with four key interpretive signals.
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers cannot reliably compare results across sources, identify what a report omits, or trace an aggregate claim to its underlying evidence. Recent efforts address isolated components but leave three gaps: they cover only narrow slices of theevaluation lifecycleand do not compose into a single interpretable record; they specify static representations that do not differentiate the questions different stakeholders bring to the same evidence; and they remain proposals on paper, lacking the extraction infrastructure required for adoption at scale. We present , an operational reporting layer that composesbenchmark metadata,evaluation run data, andmodel metadatainto a unified record. We (1) derive areporting schemafrom a structured review of 52 papers and 10 stakeholder interviews, (2) implement fourinterpretive signals(reproducibility,documentation completeness,provenanceandrisk, andscore comparability), rendered throughreader modescalibrated to research and non-research audiences, and (3) deploy amonitoring toolthat applies across 5,816 models, 635 benchmarks, and 101,843 results, surfacing systematic gaps in current reporting practice.
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