@inproceedings{zhao-etal-2026-mentor,
    title = "{MENTOR}: Efficient Autoregressive Image Generation with Balanced Multimodal Control",
    author = "Zhao, Haozhe  and
      Cai, Zefan  and
      Si, Shuzheng  and
      Chen, Liang  and
      Gu, Jiuxiang  and
      Xiao, Wen  and
      Zhang, Minjia  and
      Hu, Junjie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1508/",
    pages = "30167--30193",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent text-to-image models achieve impressive visual quality but still face challenges in precise controllability, balancing multimodal inputs, and high training cost for multimodal image generation. To address these limitations, we propose MENTOR, an autoregressive (AR) framework with a two-stage training paradigm for controllable multimodal image generation: (1) a multimodal alignment stage that establishes robust pixel and semantic-level alignment between inputs and generated tokens, followed by (2) a multimodal instruction tuning stage that balance model{'}s integration of multimodal inputs and enhance generation controllability. Extensive experiments on DreamBench++ and DreamBench demonstrate that, despite modest model size and training resources, achieves a strong balance between textual and visual guidance for controllable image generation, delivering competitive performance at significantly lower computational cost compared to leading baselines. Moreover, our approach attains superior image reconstruction fidelity, broad adaptability across different tasks, and training efficiency."
}