Elamur Research⌕
☰
home / authors / Haoran Luo

Haoran Luo

Haoran Luo has 4 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Erik Cambria, Jiapu Wang, Rui Mao.

4 papers in our library · 2026

OpenAlex · h-index 1 · 5 works · 1 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 20 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8975

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

🎓Nanyang Technological UniversitySingapore · Singapore2026 · 4 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 · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workPolitical Science and International Relations · 1 workSocial Sciences · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksReasoning · 2 worksEvaluation & benchmarks · 1 workLegal AI · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026 · 8 citations)
    Test-time scaling (TTS) makes LLMs reason better but tends to cause overthinking: extra compute goes to every reasoning step, including trivial ones.
  2. TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning (2026 · 8 citations)
    RL methods such as GRPO strengthen LLM reasoning, but they learn only from rollouts the model samples freely from its own distribution.
  3. Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026 · 3 citations)
    Many people struggle to write good prompts for hard problems, which keeps large language models from performing at their best.
  4. LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026 · 2 citations)
    Current legal benchmarks for LLMs mostly score final answers on technical tasks. They tend to ignore "soft" legal skills such as ethical judgment and assessing social impact, and their static public data may have leaked into training sets. LexGenius is a Chinese expert-level benchmark designed to probe legal general…

All works

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

  1. MUR: Momentum Uncertainty guided Reasoning for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hang Yan, Fangzhi Xu, Rongman Xu, et al.2026 · 8 citations
  2. TemplateRL: Structured Template-Guided Reinforcement Learning for LLM ReasoningFindings of the Association for Computational LinguisticsJinyang Wu, Chonghua Liao, Mingkuan Feng, et al.2026 · 8 citations
  3. Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement LearningFindings of the Association for Computational LinguisticsWenjin Liu, Haoran Luo, Xueyuan Lin, et al.2026 · 3 citations
  4. LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General IntelligenceFindings of the Association for Computational LinguisticsWenjin Liu, Haoran Luo, Xin Feng, et al.2026 · 2 citations
  5. Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJinyang Wu et al.2026
  6. MAXS: Meta-Adaptive Exploration with LLM Agents ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetJian Zhang et al.2026

 Co-authors here

12 of 22 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.

 Cited by

Papers of our library cite their works 20 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

↑ back to top

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