Trustworthy Machine Reasoning with Foundation Models

at AAAI 2026

Singapore

Bo Han
Bo Han
Hong Kong Baptist
University
Xiang Yue
Sanmi Koyejo
Stanford University
Alisa Liu
Zhanke Zhou
Hong Kong Baptist
University
Yizhong Wang
Chentao Cao
Hong Kong Baptist
University
Brando Miranda
Brando Miranda
Stanford University
Brando Miranda
Pan Lu
Stanford University

Abstract

In this tutorial, we chart a practical path from raw capability to trustworthy reasoning with foundation models. We begin by motivating why trustworthy reasoning is essential: when models bluff multiplications or invent drug interactions, their value collapses and risks increase. We adopt four pillars of trustworthiness, i.e., capability, safety, robustness, and explainability, as the organizing framework for the entire session.

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