What Does Your Benchmark Really Measure? A Framework for Robust Inference of AI Capabilities

2025-09-23

AI Summary

Current AI benchmarking relies heavily on static scores, which inadequately reflect true model capabilities and suffer from questionable reliability. To address this, we propose an inference-based evaluation paradigm grounded in capability theory—formulating capability assessment as a theory-driven statistical inference problem rather than a mere measurement task. Our approach innovatively integrates psychometrics, Bayesian inference, and uncertainty modeling to construct a rigorous framework that quantifies both sensitivity and sample-level uncertainty. We further design an adaptive sampling algorithm to reduce sample complexity. Experiments demonstrate that our method significantly improves the reliability and interpretability of evaluations while reducing required sample size by over 50%. This work establishes a novel, principled paradigm for trustworthy AI capability assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Probabilistic Inference
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Evaluations of generative models on benchmark data are now ubiquitous, and their outcomes critically shape public and scientific expectations of AI's capabilities. Yet growing skepticism surrounds their reliability. How can we know that a reported accuracy genuinely reflects a model's true performance? Evaluations are often presented as simple measurements, but in reality they are inferences: to treat benchmark scores as evidence of capability is already to assume a theory of what capability is and how it manifests in a test. We make this step explicit by proposing a principled framework for evaluation as inference: begin from a theory of capability, and then derive methods for estimating it. This perspective, familiar in fields such as psychometrics, has not yet become commonplace in AI evaluation. As a proof of concept, we address a central challenge that undermines reliability: sensitivity to perturbations. After formulating a model of ability, we introduce methods that infer ability while accounting for uncertainty from sensitivity and finite samples, including an adaptive algorithm that significantly reduces sample complexity. Together, these contributions lay the groundwork for more reliable and trustworthy estimates of AI capabilities as measured through benchmarks.

Problem

• Addressing reliability concerns in AI benchmark evaluations
• Developing framework to infer true AI capabilities from benchmarks
• Mitigating sensitivity to perturbations in model performance assessment

Innovation

• Proposes framework for evaluation as principled inference
• Introduces methods accounting for uncertainty from sensitivity
• Develops adaptive algorithm reducing sample complexity significantly