[
    {
        "id": "osp-16903",
        "type": "article-journal",
        "title": "Adversarially Trained Linear Transformers Are Optimal Robust In-Context Learners for Gaussian Mixtures",
        "author": [
            {
                "family": "Kumano",
                "given": "Soichiro"
            }
        ],
        "URL": "https://omanscience.com/en/articles/adversarially-trained-linear-transformers-are-optimal-robust-in-context-learners-for-gaussian-mixtures",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Adversarial training is one of the most reliable defenses against adversarial attacks, but its high computational cost must generally be paid anew for each task. Robust foundation models offer a promising alternative: adversarially pretrain a model once and then transfer its robustness to downstream tasks through lightweight adaptation. However, a fundamental question remains open: can robustness acquired during pretraining transfer to unseen tasks without further adversarial training? In this study, we answer this question affirmatively. A single model adversarially pretrained at scale can achieve optimal robustness on new tasks without additional task-specific training. Specifically, we show that, for a family of Gaussian-mixture classification tasks, a sufficiently deep linear transformer adversarially trained across tasks can asymptotically attain the robust Bayes error on previously unseen tasks through in-context learning from clean demonstrations. By contrast, a standardly trained model cannot. We further analyze convergence under gradient flow, an accuracy--robustness trade-off, and demonstration complexity."
    }
]