Elamur Research⌕
☰
home / authors / Ting Zhang

Ting Zhang

Ting Zhang has 2 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning, Vision & language; often with Guixian Xu, Mingzhe Du, Xin Zhou.

2 papers in our library · 2026

OpenAlex · h-index 1 · 4 works · 1 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13163

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Monash UniversityMelbourne · Australia2026 · 2 paperscurrent

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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workSoftware · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026 · 1 citation)
    SeCuRepair targets automated vulnerability repair (AVR) for C/C++ functions. The authors argue that supervised fine-tuning (SFT) teaches models to copy one reference patch. Valid fixes that are written differently get penalised, so models latch onto surface cues such as variable names. Their alternative is a…
  2. FTibSuite: A Comprehensive Resource Suite for Tibetan Vision–Language Modeling (2026)
    Tibetan has been largely left out of recent progress in vision–language models. It lacks image–text data, multimodal instruction sets and standardized benchmarks, so Tibetan VLM results are hard to reproduce or compare. The authors address this with FTibSuite, which has three parts:

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/ting-zhang/