Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
arXiv:2605.31278v1 Announce Type: cross Abstract: Reliable evaluation of agentic systems requires unbiased estimates with valid uncertainty, but standard practice navigates between costly human annotation and biased LLM-as-judge proxies. Prediction-powered inference (PPI) combines both into debiased estimates with valid confidence intervals, yet its various methods remain scattered across papers under partial implementations. We introduce GLIDE, an open-source Python library that unifies state-of-the-art PPI estimators (PPI++, Stratified PPI, Predict-Then-Debias and its stratified variants, Ac
The proliferation of advanced AI models and agentic systems creates an urgent need for reliable evaluation methods to move beyond biased proxies or costly human annotation.
Effective and unbiased evaluation is critical for the industrialization and trustworthy deployment of GenAI and agentic systems, particularly as they assume more autonomous roles.
The introduction of GLIDE provides a unified, open-source framework for Prediction-Powered Inference, potentially standardizing and accelerating reliable evaluation of AI agents.
- · AI developers
- · Enterprises deploying AI agents
- · AI safety researchers
- · Open-source AI community
- · Organizations relying solely on LLM-as-judge for evaluation
- · Proprietary, siloed AI evaluation methods
GLIDE standardizes advanced methods for evaluating agentic systems, reducing development costs and improving reliability.
More trustworthy AI agents accelerate their adoption in critical applications, driving productivity gains across various sectors.
Increased reliability mandates and standardized evaluation lead to regulatory frameworks that explicitly reference PPI methodologies for AI agent certification.
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Read at arXiv cs.LG