--- title: "Who cites Transfusion: Predict the Next — 77 citing papers" description: "77 papers of the Elamur Research library cite «Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model», found…" canonical: "https://research.elamur.ai/free/en/papers/zhou-2024-transfusion-predict-next-token-diffuse/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhou-2024-transfusion-predict-next-token-diffuse/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Transfusion: Predict the Next Token and Diffuse…](https://md.research.elamur.ai/free/en/papers/zhou-2024-transfusion-predict-next-token-diffuse/) / cited by # Who cites «Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model» 77 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model»](https://md.research.elamur.ai/free/en/papers/zhou-2024-transfusion-predict-next-token-diffuse/) 1. **[Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion](https://md.research.elamur.ai/free/en/papers/huang-2025-self-forcing-bridging-train-test/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xun Huang, Zhengqi Li, Guande He, et al.*2025 · 146 citations* 2. [**Hallucination of Multimodal Large Language Models: A Survey arXiv:2404.18930**Zechen Bai, Pichao Wang, Tianjun Xiao, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-hallucination-multimodal-large-language-models/) 3. [**Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling arXiv:2501.17811**Xiaokang Chen, Zhiyu Wu, Xingchao Liu, et al.*2025 · 112 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-janus-pro-unified-multimodal/) 4. **[Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://md.research.elamur.ai/free/en/papers/wu-2024-janus-decoupling-visual-encoding/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Chengyue Wu, Xiaokang Chen, Zhiyu Wu, et al.*2024 · 112 citations* 5. **[JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation](https://md.research.elamur.ai/free/en/papers/ma-2024-janusflow-harmonizing-autoregression-rectified/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Yiyang Ma, Xingchao Liu, Xiaokang Chen, et al.*2024 · 52 citations* 6. **[Personalized Generation In Large Model Era: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2025-personalized-generation-large-model-era/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyan Xu, Jinghao Zhang, Alireza Salemi, et al.*2025 · 24 citations* 7. [**HunyuanImage 3.0 Technical Report arXiv:2509.23951**Tencent Hunyuan Foundation Model Team*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-hunyuan-hunyuanimage-3-0-technical/) 8. **[UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision](https://md.research.elamur.ai/free/en/papers/fang-2026-unicorn-self-improving-unified-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhen Fang, Ruiyan Han, XinYu Sun, et al.*2026 · 11 citations* 9. **[FactVerse: A Benchmark for Factual Consistency in Interleaved Image–Text Generation](https://md.research.elamur.ai/free/en/papers/shan-2026-factverse-benchmark-factual-consistency-interleaved/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yubo Shan, Kun Zhang, Qiming Xu, et al.*2026* 10. **[Towards Unified Multimodal Large Language Models: A survey](https://md.research.elamur.ai/free/en/papers/ma-2026-unified-multimodal-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Ma, Yitian Zhang, Yun Fu*2026* 11. **[UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding](https://md.research.elamur.ai/free/en/papers/xu-2026-unicm-unified-consistency-model-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenkai Xu, Xu Wang, Zhenyi Liao, et al.*2026* 66 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhou-2024-transfusion-predict-next-token-diffuse/) — Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model