{"type":"ScholarlyArticle","title":"Toward Trustworthy Foundation Models: Mitigating OOD Failure in Adaptation and Deployment","authors":[{"name":"Ping Song","slug":"ping-song","given":"Ping","family":"Song","affiliation":""}],"abstract":"Foundation models’ versatile generalization has driven their broad adoption, yet their deployment is constrained by critical gaps in trustworthiness. First, we conduct a survey to present a comprehensive analysis of what constitute trustworthy foundation model and examined approaches for each requirement from a lifecycle perspective. We identify out-of-distribution (OOD) robustness as the primary technical bottleneck, where performance collapse under distribution shifts invalidates other pillars like fairness and accountability.  The nature of this vulnerability is two-fold: first, a reliance on spurious background cues during inference, and second, dimensional collapse in the representation space during fine-tuning. This research aims to mitigate these failures by developing mechanistic insights and algorithmic solutions during foundation model adaptation and deployment phases of the lifecycle.","doi":"10.24963/ijcai.2026/948","url":"https://ijcai.org/proceedings/2026/948","pdf":"https://ijcai.org/proceedings/2026/0948.pdf","markdown":"https://ijcai.org/proceedings/2026/948.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/948","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":948,"pages":{"first":8339,"last":8340},"keywords":["Machine Learning: Robustness","Machine Learning: Representation learning","Machine Learning: Trustworthy machine learning","AI Ethics, Trust, Fairnes: Trustworthy AI"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}