🧑‍🔬 Xiao Luo

🏛️ Affiliations

🎓University of Wisconsin–MadisonMadison · United States2025–2026 · 60 papersEmployment · Assistant Professorcurrent🎓University of California, Los AngelesLos Angeles · United States2022–2026 · 107 papers🎓Peking UniversityBeijing · China2019–2025 · 35 papers🏢Alibaba GroupHangzhou · China2020–2021 · 11 papers🏢Alibaba DAMO AcademyHangzhou · China2020–2021 · 10 papers🎓Nankai UniversityTianjin · China2026 · 3 papers🎓Toyota Technological Institute at ChicagoChicago · United States2022–2024 · 3 papers🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2023–2024 · 2 papers

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. Large Language Model Agent: A Survey on Methodology, Applications and Challenges (2025 · 62 citations)
   Work on LLM-based agents has grown quickly but remains scattered. Existing reviews tend to cover one slice, such as games, deployment settings, multimodality or security, or they give broad overviews without a method-level breakdown. Luo et al. address this with a survey built around how agents are engineered. Its…
2. TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer (2023 · 33 citations)
   Linear attention promises to remove the quadratic cost of softmax attention. In practice, earlier linear-attention models have trailed Transformers in language-modeling quality and rarely ran faster on real hardware. The authors close this gap with TransNormerLLM, a redesigned version of their TransNormer…
3. FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation (2025 · 19 citations)
   FinMME addresses the lack of specialised benchmarks for testing multimodal large language models (MLLMs) on financial material, where charts are data-dense and interpretation requires expert knowledge.
4. Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs (2025 · 17 citations)
   Large language models face two linked weaknesses. Their stored knowledge is fixed and can be wrong, and they struggle with problems that need several reasoning steps. Retrieval-Augmented Generation (RAG) addresses the first, and reasoning techniques such as Chain-of-Thought address the second. This survey argues that…
5. A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025 · 14 citations)
   Post-training is the stage where LLMs acquire instruction following, alignment, reasoning and domain skills.
6. Context-attended Adversarial Reinforcement Learning for Robust Multi-step Retrieval Augmented Generation (2026 · 1 citation)
   Multi-step RAG systems retrieve evidence over several rounds. The resulting long contexts gather irrelevant noise and can also contain deliberately planted misinformation. Earlier defenses such as fine-tuning on fixed noisy data, verifier agents, or self-reflective reasoning were mostly tested with a single retrieval…
7. Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026 · 1 citation)
   Reinforcement learning for LLM reasoning usually depends either on expensive human-verified answers or on label-free rewards such as majority voting and entropy.
8. InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026)
   Fine-tuning LLMs on entire instruction datasets is expensive and yields diminishing returns. Existing data-selection techniques also tend to work only in the domain they were designed for: methods tuned for general instruction-following transfer poorly to reasoning, and vice versa. InstructDiff addresses this with a…

All works

11 works of theirs in our library, the most cited first; 8 with a page here, the others link their original until we summarise them.

1. Large Language Model Agent: A Survey on Methodology, Applications and ChallengesarXiv:2503.21460Junyu Luo, Weizhi Zhang, Ye Yuan, et al.2025 · 62 citations
2. TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormerarXiv:2307.14995Zhen Qin, Dong Li, Weigao Sun, et al.2023 · 33 citations
3. FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Junyu Luo, Zhizhuo Kou, Liming Yang, et al.2025 · 19 citations
4. Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMsarXiv:2507.09477Yangning Li, Weizhi Zhang, Yuyao Yang, et al.2025 · 17 citations
5. A Survey on Efficient Large Language Model Training: From Data-centric PerspectivesAnnual Meeting of the Association for Computational Linguistics (ACL)Junyu Luo, Bohan Wu, Xiao Luo, et al.2025 · 14 citations
6. Context-attended Adversarial Reinforcement Learning for Robust Multi-step Retrieval Augmented GenerationFindings of the Association for Computational LinguisticsYingtao Ren, Xiao Luo, Yu-Cheng Chang, et al.2026 · 1 citation
7. Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement LearningFindings of the Association for Computational LinguisticsZhiyin Yu, Bo Zhang, Qibin Hou, et al.2026 · 1 citation
8. InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Junyou Su, He Zhu, Xiao Luo, et al.2026
9. GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetZhongwei Xie et al.2026
10. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZhiyin Yu et al.2026
11. DANCE: Diversity-attended Dynamic Caching with Asymmetric Quantization for Test-time Adaptation of Vision-Language Models ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetShunge Zou et al.2026

🤝 Co-authors here

12 of 39 co-authors with a page, the most papers together first.

• Junyu Luo ×4
• Bohan Wu ×2
• Ming Zhang ×2
• Weizhi Zhang ×2
• Yusheng Zhao ×2
• Zhiping Xiao ×2
• Bo Zhang
• Chengzhong Liu
• Chunkit Chan
• Dong Li
• Guanhua Chen
• He Zhu

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) ×4: ACL 2026, ACL 2025 ×2
• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
• Findings of the Association for Computational Linguistics: ACL 2026 ×2

🔗 Cited by

Papers of our library cite their works 116 times (of the 149,439 papers whose bibliographies we read); 37 citing papers with a page here →.

Updated 1 November 2026

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