--- title: "Who cites Self Forcing: Bridging the — 104 citing papers" description: "104 papers of the Elamur Research library cite «Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion», found in their…" canonical: "https://research.elamur.ai/free/en/papers/huang-2025-self-forcing-bridging-train-test/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/huang-2025-self-forcing-bridging-train-test/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Self Forcing: Bridging the Train-Test Gap in…](https://md.research.elamur.ai/free/en/papers/huang-2025-self-forcing-bridging-train-test/) / cited by # Who cites «Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion» 104 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «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/) 1. **[Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production](https://md.research.elamur.ai/free/en/papers/ye-2026-hybrid-autoregressive-diffusion-model-real/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Maoxiao Ye, Xinfeng Ye, Sathiamoorthy Manoharan*2026* 2. **[MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation](https://md.research.elamur.ai/free/en/papers/shi-2026-msvbench-human-level-evaluation-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoyuan Shi, Yunxin li, Nanhao Deng, et al.*2026* 102 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/huang-2025-self-forcing-bridging-train-test/) — Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion