--- title: "Who cites Diffusion vs. Autoregressive — 8 citing papers" description: "8 papers of the Elamur Research library cite «Diffusion vs. Among them: One Token Is Enough: Improving Diffusion Language Models with a…" canonical: "https://research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Diffusion vs. Autoregressive Language Models: A…](https://md.research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/) / cited by # Who cites «Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective» 8 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 «Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective»](https://md.research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/) 1. **[One Token Is Enough: Improving Diffusion Language Models with a Sink Token](https://md.research.elamur.ai/free/en/papers/zhang-2026-one-token-is-enough-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihou Zhang, Zheyong Xie, Li Zhong, et al.*2026 · 2 citations* 2. **[A Survey of Reasoning-Intensive Retrieval: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/wei-2026-survey-reasoning-intensive-retrieval-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyang Wei, Tingyu Song, Siyue Zhang, et al.*2026* 6 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/) — Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective