The Ephemeral Nature of Benchmarks

Evaluation scores in AI are frequently treated as objective, immutable truths about a model's capability. However, this paper argues that these scores are actually 'perishable knowledge claims.' A score is only valid within a specific context—a snapshot of a model, a specific version of a dataset, and a particular evaluation methodology. As the underlying ecosystem shifts, the relevance and accuracy of these scores decay.

Drivers of Score Degradation

Several factors contribute to the rapid obsolescence of evaluation metrics:

• Data Contamination: As benchmarks become widely available, they inevitably leak into the training sets of future models, leading to inflated performance that does not reflect true generalization.
• Distributional Drift: The real-world data that models encounter changes over time. A model that performs well on a static benchmark may fail when faced with evolving user inputs or new linguistic patterns.
• Methodological Instability: Changes in prompting strategies, evaluation frameworks, or even the underlying hardware/software stack can introduce variance that makes comparing models across different time periods or environments misleading.

Shifting Toward Dynamic Evaluation

Rather than relying on static leaderboards, the authors suggest that the community must treat evaluation as a continuous process rather than a one-time event. This involves:

• Versioning Evaluations: Treating evaluation datasets and protocols with the same rigor as software versioning, ensuring that results are reproducible and traceable.
• Temporal Awareness: Acknowledging that a score from six months ago is likely less reliable than a current one, and developing methods to weight or discount older performance data.
• Contextual Transparency: Reporting scores alongside the specific environmental and methodological metadata required to interpret them, rather than presenting them as isolated, universal metrics.