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[2602.12356] A Theoretical Framework for Adaptive Utility-Weighted Benchmarking
Summary
This paper presents a theoretical framework for adaptive utility-weighted benchmarking in AI, emphasizing the importance of stakeholder perspectives in evaluation metrics.
Why It Matters
As AI systems become more integrated into various applications, traditional benchmarking methods may not adequately capture the complexities of model performance across different contexts. This framework aims to enhance accountability and alignment with human values in AI evaluations, making it a significant contribution to the field.
Key Takeaways
- Introduces a multilayer framework for benchmarking that incorporates stakeholder priorities.
- Utilizes human-in-the-loop methods to adapt benchmarks dynamically.
- Aims to enhance the interpretability and stability of evaluation metrics.
- Generalizes classical leaderboards to accommodate evolving AI contexts.
- Promotes more accountable and human-aligned evaluation practices.
Computer Science > Artificial Intelligence arXiv:2602.12356 (cs) [Submitted on 12 Feb 2026] Title:A Theoretical Framework for Adaptive Utility-Weighted Benchmarking Authors:Philip Waggoner View a PDF of the paper titled A Theoretical Framework for Adaptive Utility-Weighted Benchmarking, by Philip Waggoner View PDF HTML (experimental) Abstract:Benchmarking has long served as a foundational practice in machine learning and, increasingly, in modern AI systems such as large language models, where shared tasks, metrics, and leaderboards offer a common basis for measuring progress and comparing approaches. As AI systems are deployed in more varied and consequential settings, though, there is growing value in complementing these established practices with a more holistic conceptualization of what evaluation should represent. Of note, recognizing the sociotechnical contexts in which these systems operate invites an opportunity for a deeper view of how multiple stakeholders and their unique priorities might inform what we consider meaningful or desirable model behavior. This paper introduces a theoretical framework that reconceptualizes benchmarking as a multilayer, adaptive network linking evaluation metrics, model components, and stakeholder groups through weighted interactions. Using conjoint-derived utilities and a human-in-the-loop update rule, we formalize how human tradeoffs can be embedded into benchmark structure and how benchmarks can evolve dynamically while preserving s...