PhD Defense: The Anatomy of Trust in Foundation Models: Representations, Reasoning, and Alignment

Foundation models demonstrate impressive capabilities, but strong performance alone does not guarantee trustworthy behavior. This dissertation examines trustworthiness across three connected dimensions: representations, reasoning, and alignment. At the representation level, it investigates how model internals shape behavior and expose vulnerabilities in safety mechanisms. At the reasoning level, it develops methods for verifiable computation and guidance during generation to improve reliability. At the alignment level, it explores training methods that improve model behavior and evaluation methods that reveal failures under practical deployment conditions. Together, these contributions connect an understanding of model internals with methods for verifying and improving behavior, advancing more reliable, interpretable, and controllable foundation models.