Researchers propose a query-efficient method for evaluating new AI models using cached responses from previously-evaluated models, leveraging the Data Kernel Perspective Space (DKPS) framework to reduce computational costs while maintaining evaluation accuracy. The approach demonstrates that by intelligently reusing existing model outputs, organizations can achieve equivalent benchmarking results with substantially fewer new queries.
This research addresses a critical efficiency problem in modern AI development: the prohibitive computational expense of evaluating new models against existing benchmarks. As AI systems grow more complex and capable, generating and assessing responses for comprehensive evaluation datasets becomes increasingly costly. The paper introduces DKPS, a black-box method that quantifies relationships between models and enables intelligent leverage of cached model responses to predict how new models will perform without exhaustive testing. The approach represents a practical solution to a genuine bottleneck in AI development pipelines. Many organizations maintain extensive caches of responses from previously-evaluated models—this work demonstrates these repositories contain actionable signal for new model evaluation. The theoretical framework establishes conditions under which DKPS-based methods achieve query efficiency, while empirical results validate that comparable mean absolute error can be obtained with significantly reduced query budgets. Beyond the technical contribution, this methodology has meaningful implications for AI development economics. Reducing evaluation costs accelerates iteration cycles and democratizes model benchmarking for resource-constrained organizations. The offline query selection method further optimizes this process by identifying which queries provide the most informative signal about reference model performance. As AI evaluation becomes increasingly expensive and central to responsible deployment, efficiency gains compound across the industry. This work contributes to making advanced model evaluation more accessible and sustainable, potentially enabling faster innovation cycles while maintaining rigorous performance assessment standards.
- →DKPS-based evaluation achieves equivalent accuracy to traditional methods while reducing query budget substantially
- →Cached responses from previously-evaluated models contain sufficient signal to predict new model performance
- →The approach provides theoretical guarantees for query efficiency under specific conditions
- →Offline query selection methodology improves prediction accuracy by identifying high-signal evaluation queries
- →Method enables more cost-effective and accessible AI model evaluation for organizations of varying scales