🧑‍🔬 Jinghui Lu

🏛️ Affiliations

🏢ByteDanceBeijing · China2023–2026 · 9 papersEmployment 2023–2025 · AI Research Scientist, AMLcurrent🎓PLA Rocket Force University of EngineeringXi'an · China2009–2012 · 5 papers🎓University of Science and Technology of ChinaHefei · China2023–2025 · 4 papers🎓University College DublinDublin · Ireland2016–2022 · 4 papersEducation 2016–2021 · Ph.D, School of Computer ScienceEmployment 2021–2022 · PostDoc, School of Computer Science🔬National Synchrotron Radiation LaboratoryHefei · China2023–2025 · 3 papers🔬Insight SFI Research Centre for Data AnalyticsDublin · Ireland2019–2022 · 3 papers🏢SensetimeBeijing · China2022–2023 · 1 paperEmployment 2022–2023 · AI Research Scientist, SCG🏢XiaomiBeijing · China2025Employment since 2025 · Senior Staff Research Scientist

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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. MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025 · 75 citations)
   Evaluating LLMs across languages has two gaps. Demanding reasoning benchmarks such as MMLU-Pro exist mainly in English. Multilingual suites like Global-MMLU combine translation sources of uneven quality and pose easier questions. The authors address both with MMLU-ProX, which carries the ten-option, reasoning-heavy…
2. Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning (2025 · 11 citations)
   Reasoning-tuned LLMs and MLLMs tend to produce long chain-of-thought output for every query. This costs many tokens and can even hurt accuracy on easy, fact-based questions. A pilot with Qwen2.5-0.5B on text-rich VQA and KIE data showed that direct answers were both more accurate and far cheaper than reasoning-based…
3. Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning (2026 · 7 citations)
   Large-scale scanning of historical Chinese books has produced many page images, but scanning alone does not make their content searchable or understandable.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model EvaluationConference on Empirical Methods in Natural Language Processing (EMNLP)Weihao Xuan, Rui Yang, Heli Qi, et al.2025 · 75 citations
2. Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM ReasoningarXiv:2505.15154Jinghui Lu, Haiyang Yu, Siliang Xu, et al.2025 · 11 citations
3. Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge ReasoningFindings of the Association for Computational LinguisticsHaiyang Yu, Yuchuan Wu, Fan Shi, et al.2026 · 7 citations
4. ToolCPT: Improving Tool Utilization in LLM Agents via Continuous Pre-training ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYifan Yang et al.2026

🤝 Co-authors here

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

• Haiyang Yu ×2
• Jingqun Tang ×2
• Aosong Feng
• Bin Li
• Fan Gao
• Han Wang
• Hao Feng
• Heli Qi
• Junjue Wang
• Qingcheng Zeng
• Rui Yang
• Siliang Xu

Venues

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

• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Findings of the Association for Computational Linguistics: ACL 2026

🔗 Cited by

Papers of our library cite their works 81 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated 31 October 2026

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