[2510.12060] Your VAR Model is Secretly an Efficient and Explainable Generative Classifier

Computer Science > Machine Learning arXiv:2510.12060 (cs) [Submitted on 14 Oct 2025 (v1), last revised 21 Mar 2026 (this version, v2)] Title:Your VAR Model is Secretly an Efficient and Explainable Generative Classifier Authors:Yi-Chung Chen, David I. Inouye, Jing Gao View a PDF of the paper titled Your VAR Model is Secretly an Efficient and Explainable Generative Classifier, by Yi-Chung Chen and 2 other authors View PDF HTML (experimental) Abstract:Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts. However, recent progress in this area has been largely driven by diffusion-based models, whose substantial computational cost severely limits scalability. This exclusive focus on diffusion-based methods has also constrained our understanding of generative classifiers. In this work, we propose a novel generative classifier built on recent advances in visual autoregressive (VAR) modeling, which offers a new perspective for studying generative classifiers. To further enhance its performance, we introduce the Adaptive VAR Classifier$^+$ (A-VARC$^+$), which achieves a superior trade-off between accuracy and inference speed, thereby significantly improving practical applicability. Moreover, we show that the VAR-based method exhibits fundamentally different properties from diffusion-based methods. In particular, due to its tractable likelihood, the VAR-based ...