🧑‍🔬 Xue Jiang

🏛️ Affiliations

🎓Shanghai Jiao Tong UniversityShanghai · China2016–2026 · 84 paperscurrent🎓Jiangsu University of Science and TechnologyZhenjiang · China2025–2026 · 3 paperscurrent🎓Peking UniversityBeijing · China2023–2026 · 3 paperscurrent🏥First Hospital of China Medical UniversityShenyang · China2025–2026 · 2 paperscurrent🏥The Affiliated Yongchuan Hospital of Chongqing Medical UniversityChongqing · China2026 · 1 papercurrent🎓McMaster UniversityHamilton · Canada2011–2015 · 13 papers🎓Arizona State UniversityTempe · United States2015–2016 · 5 papers🎓Huazhong Agricultural UniversityWuhan · China2023–2025 · 4 papers🎓Shandong University of Science and TechnologyQingdao · China2009–2012 · 4 papers🏛️Chinese Academy of SciencesBeijing · China2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 17 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization (2025 · 10 citations)
   Large language models can infer personal details about an author, such as age, gender, income or location, from seemingly harmless text.
2. RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026 · 6 citations)
   RL-PLUS targets a known weakness of Reinforcement Learning with Verifiable Reward (RLVR). Standard on-policy training such as GRPO raises pass@1 but often lowers pass@k at large k. In other words, the tuned model solves fewer distinct problems than its base model, which the authors call capability boundary collapse.…
3. CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment (2026 · 5 citations)
   LLMs learn code by predicting text, but whether a program is correct depends on how it behaves when run. RLVR methods try to close this gap by rewarding programs that pass tests. A single pass/fail bit, however, gives little guidance about subtle logic errors such as mistracked loop variables.
4. PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information Leakage (2026)
   Aligned LLMs usually decline questions about sensitive traits of real people, but they may still hold such facts from pre-training.

🤝 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

• Krishna Kanth Nakka ×2
• Xuebing Zhou ×2
• Yihong Dong ×2
• Yongding Tao ×2
• Zhi Jin ×2
• Ahmed Frikha
• Binhua Li
• Fei Huang
• Ge Li
• Kechi Zhang
• Lili Mou
• Rongyu Cao

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) ×2: ACL 2026 ×2
• International Joint Conference on Natural Language Processing (IJCNLP): IJCNLP 2025
• Workshop on Trustworthy NLP: Workshop on Trustworthy NLP 2026

🔗 Cited by

Papers of our library cite their works 18 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated 1 November 2026

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