📄ArXiv AI•May 21, 2026Stalecollected in 40m

Formalizing Trust Calibration for Agentic Tool Use

#agentic-ai #trust-calibration #human-in-the-loop #preference-learningagentic-tool-use-frameworkgaussian-process agentic-systems

💡Learn a new mathematical framework to safely balance agent autonomy and human oversight using preference learning.

⚡ 30-Second TL;DR

What Changed

Models trust calibration as a preference-learning problem using binary human feedback.

Why It Matters

This framework provides a mathematically grounded approach to human-in-the-loop AI, reducing the risk of over-automation. It helps developers build safer agentic workflows by dynamically adjusting autonomy based on real-time uncertainty.

What To Do Next

Implement a uncertainty-aware policy gateway in your agentic pipeline using Gaussian-process classification to determine when to trigger human-in-the-loop verification.

Who should care:Researchers & Academics

Key Points

•Models trust calibration as a preference-learning problem using binary human feedback.
•Utilizes Gaussian-process posterior to identify high-uncertainty action regions.
•Implements a policy gateway to classify actions into allow, block, or ask categories.
•Optimizes for sample-efficient uncertainty-targeted querying rather than pure design optimization.

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