A new preprint on arXiv examines how AI evaluation fits into a broader ecosystem. The authors note that evaluation results influence decisions made by model providers, users, funders, and regulators. They argue that designing valid benchmarks requires more than technical rigor; it requires understanding how these actors interact and depend on each other.
The paper's central claim is that benchmark design choices should be contextualized within the dynamics of this ecosystem. Without that context, a benchmark may appear sound in isolation but fail to serve the needs of the people and institutions relying on it. The abstract stops short of describing the framework the authors develop, so the full proposal is not available from the source.
As an arXiv preprint, the work has not yet undergone peer review. Readers interested in the complete argument will need to consult the full paper.