--- title: "Xianfeng Tang — author profile and papers — Elamur Research" description: "Xianfeng Tang, Amazon: 7 papers on Elamur Research (2024–2026), h-index 6 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/xianfeng-tang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/xianfeng-tang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Xianfeng Tang # Xianfeng Tang Xianfeng Tang has 7 papers in our library, in 2024–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Fali Wang](https://md.research.elamur.ai/free/en/authors/fali-wang/), [Hui Liu](https://md.research.elamur.ai/free/en/authors/hui-liu/), [Qi He](https://md.research.elamur.ai/free/en/authors/qi-he/). 7 papers in our library · 2024–2026 [OpenAlex](https://openalex.org/A5102008510) · [ORCID](https://orcid.org/0000-0003-1554-2761) · [Wikidata](https://www.wikidata.org/wiki/Q130934866) · [**h-index 6**](https://openalex.org/authors/A5102008510) · [31 works](https://openalex.org/works?filter=author.id:A5102008510) · [190 citations](https://openalex.org/works?filter=author.id:A5102008510&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 7 works in our library](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/#papers) · [🔗 Cited 211 times in our library](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/cited-by/) · [⭐ Paper Stars ranking: #4961](https://md.research.elamur.ai/free/en/paper-stars/page/50/#star-4961) 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=Xianfeng+Tang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Xianfeng+Tang) · [dblp](https://dblp.org/search?q=Xianfeng+Tang) · [ACL Anthology](https://aclanthology.org/search/?q=Xianfeng+Tang) ## Affiliations [🏢**Amazon** Seattle · United States 2023–2026 · 16 papers*current*](https://md.research.elamur.ai/free/en/companies/amazon/) · [🎓**Chengdu College of University of Electronic Science And Technology of China** Chengdu · China 2026 · 1 paper*current*](https://ror.org/0401m8x58) · [🎓**Southwest Jiaotong University** Chengdu · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/southwest-jiaotong-university/) · [🎓**University of Electronic Science and Technology of China** Chengdu · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) · [🎓**Harbin Institute of Technology** Harbin · China 2005–2013 · 4 papers](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 7 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 7 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 7 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 7 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 4 Research subjects and languages the work is about.[Language models & pretraining · 4 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Reasoning · 3 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Vision & language · 2 works](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/year/2026/) · [**2025** 3 papers](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/year/2025/) · [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/)** *(2024 · 159 citations)* LLMs such as Llama-3.1 405B are costly to run and fine-tune. They usually sit behind cloud APIs, which creates privacy risks, and they respond too slowly for on-device or real-time use. They can also fall short in specialised fields like medicine or law. This survey examines small language models (SLMs) as an… 2. **[Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs](https://md.research.elamur.ai/free/en/papers/jin-2024-graph-chain-thought-augmenting-large/)** *(2024 · 89 citations)* LLMs often hallucinate on knowledge-heavy questions. Standard retrieval augmentation treats each document as a separate unit, so it misses knowledge carried by links between texts, such as citations, co-purchases or legal references. Turning whole neighbourhoods into text is also impractical, because subgraphs grow… 3. **[Retrieval-Augmented Generation with Graphs (GraphRAG)](https://md.research.elamur.ai/free/en/papers/han-2025-retrieval-augmented-generation-graphs-graphrag/)** *(2025 · 87 citations)* Conventional retrieval-augmented generation (RAG) stores knowledge as separate text or image chunks and finds them through lexical or embedding similarity. 4. **[SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-sft-rl-early-investigation-training/)** *(2025 · 37 citations)* The authors examine whether the common recipe of supervised fine-tuning followed by reinforcement learning, popularised by DeepSeek-R1, also works for Large Vision-Language Models (LVLMs). 5. **[ViLBench: A Suite for Vision-Language Process Reward Modeling](https://md.research.elamur.ai/free/en/papers/tu-2025-vilbench-suite-vision-language-process/)** *(2025 · 9 citations)* Process reward models (PRMs) score every reasoning step rather than only the final answer, which helps pick good solution paths. 6. **[Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents](https://md.research.elamur.ai/free/en/papers/wang-2026-trajectory2task-training-robust-tool-calling/)** *(2026 · 2 citations)* Tool-calling agents used in customer service often meet users whose requests lack key details, shift during the conversation, or break system policy. 7. **[Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations](https://md.research.elamur.ai/free/en/papers/luo-2026-graph-assisted-large-language-models/)** *(2026)* This survey asks how graph structures can offset three built-in weaknesses of large language models. The first is knowledge: static training leads to outdated facts, errors, and unwanted memorization. The second is reasoning: token-by-token generation produces linear chains that cannot branch or backtrack. The third… ## Co-authors here 12 of 41 co-authors with a page, the most papers together first. - [Fali Wang](https://md.research.elamur.ai/free/en/authors/fali-wang/) ×3 - [Hui Liu](https://md.research.elamur.ai/free/en/authors/hui-liu/) ×3 - [Qi He](https://md.research.elamur.ai/free/en/authors/qi-he/) ×3 - [Cihang Xie](https://md.research.elamur.ai/free/en/authors/cihang-xie/) ×2 - [Haoqin Tu](https://md.research.elamur.ai/free/en/authors/haoqin-tu/) ×2 - [Hardy Chen](https://md.research.elamur.ai/free/en/authors/hardy-chen/) ×2 - [Suhang Wang](https://md.research.elamur.ai/free/en/authors/suhang-wang/) ×2 - [Xianren Zhang](https://md.research.elamur.ai/free/en/authors/xianren-zhang/) ×2 - [Zhiwei Zhang](https://md.research.elamur.ai/free/en/authors/zhiwei-zhang/) ×2 - [Bowen Jin](https://md.research.elamur.ai/free/en/authors/bowen-jin/) - [Chen Luo](https://md.research.elamur.ai/free/en/authors/chen-luo/) - [Chulin Xie](https://md.research.elamur.ai/free/en/authors/chulin-xie/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/), [Findings of the Association for Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/findings/2024/) - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) - [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/): [ACM Transactions on Intelligent Systems and Technology 2024](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/2024/) - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/) ## Cited by Papers of our library cite their works 211 times (of the 149,439 papers whose bibliographies we read); [49 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/cited-by/). Updated 2 November 2026