--- title: "Yulong Wang — author profile and papers — Elamur Research" description: "Yulong Wang, Beijing University of Posts and Telecommunications: 2 papers on Elamur Research (2026), h-index 0 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/yulong-wang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yulong-wang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yulong Wang # Yulong Wang Yulong Wang has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5139722765) · [**h-index 0**](https://openalex.org/authors/A5139722765) · [4 works](https://openalex.org/works?filter=author.id:A5139722765) · [0 citations](https://openalex.org/works?filter=author.id:A5139722765&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/yulong-wang/#papers) · [⭐ Paper Stars ranking: #14092](https://md.research.elamur.ai/free/en/paper-stars/page/141/#star-14092) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yulong+Wang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yulong+Wang) · [dblp](https://dblp.org/search?q=Yulong+Wang) · [ACL Anthology](https://aclanthology.org/search/?q=Yulong+Wang) ## Affiliations [🎓**Beijing University of Posts and Telecommunications** Beijing · China 2026 · 4 papers*current*](https://md.research.elamur.ai/free/en/universities/beijing-university-of-posts-and-telecommunications/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 3 Research subjects and languages the work is about.[Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language · 1 work](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/yulong-wang/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Reference Attack: A New Cross-Modal Jailbreaking Attack against Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-reference-attack-new-cross-modal/)** *(2026)* The paper presents Reference Attack, a black-box jailbreak technique for multimodal large language models (MLLMs). 2. **[TARE: Lightweight Token-Aware Representation Editing for Fine-tuning Transformer-like Models](https://md.research.elamur.ai/free/en/papers/wang-2026-tare-lightweight-token-aware-representation/)** *(2026)* Parameter-efficient fine-tuning (PEFT) involves a trade-off. Low-rank adapters such as LoRA add many trainable weights and need rank and scale settings. Cheap representation editors such as RED apply one transformation to every token and miss token-level context. The authors observe that the per-token optimal scaling… ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2 Updated 1 November 2026