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Multimodal Large Language Models for Multi-Subject In-Context Image Generation

Paper · 2026 · Yucheng Zhou, Dubing Chen, Huan Zheng et al.

Summary

Personalized text-to-image systems that must place several user-supplied subjects into one scene tend to break down as the number of references grows. Some subjects disappear, and the scene drifts from the prompt. The authors present MUSIC, which they describe as the first multimodal LLM built specifically for multi-subject in-context image generation. Suitable training data is scarce, so they build a fully automatic pipeline that chains foundation models to synthesize training examples without raw data or manual labels. These models are an LLM, a T2I generator, a VLM, an image-to-image model, open-vocabulary detection and segmentation. MUSIC reasons through a vision chain-of-thought, moving from subject images to their semantics and then to the output. It also uses semantics-driven spatial layout planning, which decides which subject owns which visual content before generation, to limit identity entanglement. Sampling several plans additionally enables test-time scaling. Complex subject images are included in training. On the new MSIC benchmark and on DreamBench, MUSIC outperforms prior methods such as UNO and OmniGen in both multi- and single-subject settings. The work shows that MLLM reasoning can help subject-driven generation scale.

Key points

  • New resource: the MSIC benchmark, manually curated from synthetic data, covering scenes with 1 to 12 reference subjects. Evaluation also uses DreamBench with DINO and CLIP-T metrics.
  • Vision CoT training data comes from Qwen2.5-VL (3B, 7B, 32B). CoT from the 32B model gives better results. Supplying ground-truth CoT at inference improves all variants.
  • Test-time scaling via Pass@N with CLIP-based selection raises scores as N grows from 2 to 16. DINO improves from 0.623 to 0.631, and CLIP-T from 0.324 to 0.330.
  • In ablations, every component helps. Layout planning and vision CoT matter most for multi-subject reasoning.
  • Limitations: quality still declines as the number of subjects increases. Pass@N inference cost grows linearly with N.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of MacauMacao · China43 works · 2,368 citations

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksVision & language · 811 works

Cite

Cite as Yucheng Zhou, Dubing Chen, Huan Zheng and Jianbing Shen. 2026. Multimodal Large Language Models for Multi-Subject In-Context Image Generation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1518

@inproceedings{zhou2026multimodal,
  title = {{Multimodal Large Language Models for Multi-Subject In-Context Image Generation}},
  author = {Yucheng Zhou, Dubing Chen, Huan Zheng and Jianbing Shen},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1518},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1518}
}

References

89 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  2. Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, and 1 others. 2022. Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems, 35:23716– 23736.
  3. Wenhu Chen, Hexiang Hu, Chitwan Saharia, and William W Cohen. 2022. Re-imagen: Retrieval-augmented text-to-image generator. arXiv preprint arXiv:2209.14491. — [arXiv:2209.14491](https://arxiv.org/abs/2209.14491) find
  4. Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780– 8794. find
  5. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, and 1 others. 2024. Scaling rectified flow transformers for high-resolution image synthesis. In ICML. find
  6. Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. 2023. All are worth words: A vit backbone for diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22669–22679. find
  7. Patrick Esser, Robin Rombach, and Bjorn Ommer. 2021. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873–12883. find
  8. Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei Efros. 2022. Visual prompting via image inpainting. Advances in Neural Information Processing Systems, 35:25005–25017. find
  9. Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, and Yonglong Tian. 2024. Fluid: Scaling autoregressive text-to-image generative models with continuous tokens. arXiv preprint arXiv:2410.13863. find
  10. Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096. — [arXiv:1809.11096](https://arxiv.org/abs/1809.11096) find
  11. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
  12. Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, and 1 others. 2023. Muse: Text-to-image generation via masked generative transformers. arXiv preprint arXiv:2301.00704. — [arXiv:2301.00704](https://arxiv.org/abs/2301.00704) find
  13. Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. 2022. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11315–11325. find
  14. Guanjie Chen, Xinyu Zhao, Yucheng Zhou, Xiaoye Qu, Tianlong Chen, and Yu Cheng. 2025. Towards stabilized and efficient diffusion transformers through long-skip-connections with spectral constraints. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 17708–17718. find
  15. Shuang Chen, Quanxin Shou, Hangting Chen, Yucheng Zhou, Kaituo Feng, Wenbo Hu, Yi-Fan Zhang, Yunlong Lin, Wenxuan Huang, Mingyang Song, and 1 others. 2026. Unify-agent: A unified multimodal agent for world-grounded image synthesis. arXiv preprint arXiv:2603.29620. — [arXiv:2603.29620](https://arxiv.org/abs/2603.29620) find
  16. Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. 2022. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618. find
  17. Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. 2023. Masked diffusion transformer is a strong image synthesizer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 23164–23173. find
  18. Yuying Ge, Sijie Zhao, Ziyun Zeng, Yixiao Ge, Chen Li, Xintao Wang, and Ying Shan. 2023. Making llama see and draw with seed tokenizer. arXiv preprint arXiv:2310.01218. — [arXiv:2310.01218](https://arxiv.org/abs/2310.01218) find
  19. Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, and Ying Shan. 2024. Seed-x: Multimodal models with unified multi-granularity comprehension and generation. arXiv preprint arXiv:2404.14396. — [arXiv:2404.14396](https://arxiv.org/abs/2404.14396) find
  20. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Communications of the ACM, 63(11):139–144. find
  21. Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. 2024. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.04431. — [arXiv:2412.04431](https://arxiv.org/abs/2412.04431) find
  22. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. NIPS, 33:6840–6851. find
  23. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, and 1 others. 2022. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3.
  24. Qihan Huang, Siming Fu, Jinlong Liu, Hao Jiang, Yipeng Yu, and Jie Song. 2025. Resolving multi-condition confusion for finetuning-free personalized image generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 3707–3714. find
  25. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276.
  26. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119. find
  27. Black Forest Labs. 2024a. Flux. https://github.com/black-forest-labs/flux. ↗
  28. Black Forest Labs. 2024b. Flux: Official inference repository for flux.1 models. Accessed: 2025-02-07. find
  29. Mengcheng Lan, Chaofeng Chen, Yue Zhou, Jiaxing Xu, Yiping Ke, Xinjiang Wang, Litong Feng, and Wayne Zhang. 2024. Text4seg: Reimagining image segmentation as text generation. arXiv preprint arXiv:2410.09855. — [arXiv:2410.09855](https://arxiv.org/abs/2410.09855) find
  30. Dongxu Li, Junnan Li, and Steven Hoi. 2023. Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing. Advances in Neural Information Processing Systems, 36:30146–30166. find
  31. Zhendong Mao, Mengqi Huang, Fei Ding, Mingcong Liu, Qian He, and Yongdong Zhang. 2024. Realcustom++: Representing images as real-word for real-time customization. arXiv preprint arXiv:2408.09744. — [arXiv:2408.09744](https://arxiv.org/abs/2408.09744) find
  32. Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. 2024. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pages 4296–4304. find
  33. Matteo Nulli, Anesa Ibrahimi, Avik Pal, Hoshe Lee, and Ivona Najdenkoska. 2024. In-context learning improves compositional understanding of vision-language models. arXiv preprint arXiv:2407.15487. — [arXiv:2407.15487](https://arxiv.org/abs/2407.15487) find
  34. Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, and 1 others. 2022. In-context learning and induction heads. arXiv preprint arXiv:2209.11895.
  35. Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, and 7 others. 20… — DINOv2: Learning Robust Visual Features without Supervision
  36. Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T Freeman, and Yu-Xiong Wang. 2024. Randar: Decoder-only autoregressive visual generation in random orders. arXiv preprint arXiv:2412.01827. — [arXiv:2412.01827](https://arxiv.org/abs/2412.01827) find
  37. William Peebles and Saining Xie. 2023. Scalable diffusion models with transformers. In ICCV, pages 4195–4205.
  38. Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021. What makes good in-context examples for gpt-3? arXiv preprint arXiv:2101.06804.
  39. Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952.
  40. Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, and 1 others. 2024. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European Conference on Computer Vision, pages 38–55. Springer.
  41. Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean. 2023. Efficiently scaling transformer inference. Proceedings of Machine Learning and Systems, 5:606–624. find
  42. Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. arXiv preprint arXiv:2104.08786.
  43. Jian Ma, Junhao Liang, Chen Chen, and Haonan Lu. 2024. Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning. In ACM SIGGRAPH 2024 Conference Papers, pages 1–12. find
  44. Senthil Purushwalkam, Akash Gokul, Shafiq Joty, and Nikhil Naik. 2024. Bootpig: Bootstrapping zero-shot personalized image generation capabilities in pretrained diffusion models. arXiv preprint arXiv:2401.13974. — [arXiv:2401.13974](https://arxiv.org/abs/2401.13974) find
  45. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, and 1 others. 2021. Learning transferable visual models from natural language supervision. In ICML, pages 8748–8763. PmLR.
  46. Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, and 1 others. 2024. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. find
  47. Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. 2019. Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems, 32. find
  48. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10684–10695.
  49. Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In CVPR, pages 22500–22510. find
  50. Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li. 2024. Visual cot: Advancing multi-modal language models with a comprehensive dataset and benchmark for chain-of-thought reasoning. Advances in Neural Information Processing Systems, 37:8612–8642.
  51. Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. 2019. Objects365: A large-scale, high-quality dataset for object detection. In CVPR, pages 8430– 8439. find
  52. XLabs AI team. 2025. x-flux. Accessed: 2025-02-07. find
  53. Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. 2024. Visual autoregressive modeling: Scalable image generation via next-scale prediction. Advances in neural information processing systems, 37:84839–84865. find
  54. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, and 1 others. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  55. Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. 2021. Multimodal few-shot learning with frozen language models. Advances in Neural Information Processing Systems, 34:200–212.
  56. Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, and 1 others. 2016. Conditional image generation with pixelcnn decoders. Advances in neural information processing systems, 29. find
  57. Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. In International conference on machine learning, pages 1747–1756. PMLR. find
  58. Aaron Van Den Oord, Oriol Vinyals, and 1 others. 2017. Neural discrete representation learning. Advances in neural information processing systems, 30. find
  59. Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML, pages 2256–2265. pmlr.
  60. Chenglin Wang, Yucheng Zhou, Shawn Chen, Tao Wang, and Kai Zhang. 2026. Ladr: Locality-aware dynamic rescue for efficient text-to-image generation with diffusion large language models. arXiv preprint arXiv:2603.13450.
  61. Han Song, Yucheng Zhou, Jianbing Shen, and Yu Cheng. 2026. From broad exploration to stable synthesis: Entropy-guided optimization for autoregressive image generation. In The Fourteenth International Conference on Learning Representations. find
  62. Xierui Wang, Siming Fu, Qihan Huang, Wanggui He, and Hao Jiang. 2024a. Ms-diffusion: Multi-subject zero-shot image personalization with layout guidance. arXiv preprint arXiv:2406.07209. — [arXiv:2406.07209](https://arxiv.org/abs/2406.07209) find
  63. Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502.
  64. Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. 2023. Consistency models. find
  65. Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. 2024. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525. — [arXiv:2406.06525](https://arxiv.org/abs/2406.06525) find
  66. Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. 2024. Ominicontrol: Minimal and universal control for diffusion transformer. arXiv preprint arXiv:2411.15098, 3. — [arXiv:2411.15098](https://arxiv.org/abs/2411.15098) find
  67. Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, and 1 others. 2024b. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869. — [arXiv:2409.18869](https://arxiv.org/abs/2409.18869) find
  68. Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. 2024. Maskbit: Embedding-free image generation via bit tokens. arXiv preprint arXiv:2409.16211. — [arXiv:2409.16211](https://arxiv.org/abs/2409.16211) find
  69. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824– 24837.
  70. Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo. 2023. Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation. In CVPR, pages 15943–15953. find
  71. Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023a. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3836–3847. find
  72. Shaojin Wu, Mengqi Huang, Wenxu Wu, Yufeng Cheng, Fei Ding, and Qian He. 2025. Less-to-more generalization: Unlocking more controllability by in-context generation. arXiv preprint arXiv:2504.02160. — [arXiv:2504.02160](https://arxiv.org/abs/2504.02160) find
  73. Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu. 2023b. What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems, 36:17773–17794. find
  74. Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Shuting Wang, Tiejun Huang, and Zheng Liu. 2024. Omnigen: Unified image generation. arXiv preprint arXiv:2409.11340. — [arXiv:2409.11340](https://arxiv.org/abs/2409.11340) find
  75. Yuxuan Zhang, Yiren Song, Jiaming Liu, Rui Wang, Jinpeng Yu, Hao Tang, Huaxia Li, Xu Tang, Yao Hu, Han Pan, and 1 others. 2024. Ssr-encoder: Encoding selective subject representation for subject-driven generation. In CVPR, pages 8069–8078. find
  76. Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. 2024. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528. — [arXiv:2408.12528](https://arxiv.org/abs/2408.12528) find
  77. Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. Calibrate before use: Improving few-shot performance of language models. In International conference on machine learning, pages 12697–12706. PMLR.
  78. Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021. An explanation of in-context learning as implicit bayesian inference. arXiv preprint arXiv:2111.02080.
  79. Yucheng Zhou, Hao Li, and Jianbing Shen. 2026a. Condition errors refinement in autoregressive image generation with diffusion loss. In The Fourteenth International Conference on Learning Representations. find
  80. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, and 22 others. 2024. Qwen2.5 technical report. arXiv preprint arXi… find
  81. Yucheng Zhou, Xiang Li, Qianning Wang, and Jianbing Shen. 2024a. Visual in-context learning for large vision-language models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 15890–15902. find
  82. Hongji Yang, Wencheng Han, Yucheng Zhou, and Jianbing Shen. 2025a. Dc-controlnet: Decoupling inter-and intra-element conditions in image generation with diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19065–19074. find
  83. Hongji Yang, Yucheng Zhou, Wencheng Han, and Jianbing Shen. 2025b. Self-rewarding large vision-language models for optimizing prompts in text-to-image generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7332– 7349. find
  84. Hongji Yang, Yucheng Zhou, Wencheng Han, Runzhou Tao, Zhongying Qiu, Jianfei Yang, and Jianbing Shen. 2025c. Hicogen: Hierarchical compositional text-to-image generation in diffusion models via reinforcement learning. arXiv preprint arXiv:2511.19965. — [arXiv:2511.19965](https://arxiv.org/abs/2511.19965) find
  85. Yucheng Zhou, Zhi Rao, Jun Wan, and Jianbing Shen. 2024b. Rethinking visual dependency in long-context reasoning for large vision-language models. arXiv preprint arXiv:2410.19732. — [arXiv:2410.19732](https://arxiv.org/abs/2410.19732) find
  86. Yucheng Zhou, Jianbing Shen, and Yu Cheng. 2025. Weak to strong generalization for large language models with multi-capabilities. In The Thirteenth International Conference on Learning Representations. find
  87. Yucheng Zhou, Jihai Zhang, Guanjie Chen, Jianbing Shen, and Yu Cheng. 2026b. Less is more: Vision representation compression for efficient video generation with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 13826–13834. find
  88. Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. 2023. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.06721. — [arXiv:2308.06721](https://arxiv.org/abs/2308.06721) find
  89. Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, and 1 others. 2022. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3):5. — [arXiv:2206.10789](https://arxiv.org/abs/2206.10789) find

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