--- title: "Who cites Run Luo — 58 papers of our library — Elamur Research" description: "58 papers of the Elamur Research library cite the works of Run Luo, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/run-luo/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/run-luo/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Run Luo](https://md.research.elamur.ai/free/en/authors/run-luo/) / cited by # Who cites Run Luo 58 citations of their works from 58 papers of our library (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← Run Luo](https://md.research.elamur.ai/free/en/authors/run-luo/) 1. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks arXiv:2412.15204**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/) 2. [**MagicPIG: LSH Sampling for Efficient LLM Generation arXiv:2410.16179**Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-magicpig-lsh-sampling-efficient-llm/) 3. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/) 4. **[ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents](https://md.research.elamur.ai/free/en/papers/wang-2025-vidorag-visual-document-retrieval-augmented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qiuchen Wang, Ruixue Ding, Zehui Chen, et al.*2025 · 39 citations* 5. **[IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization](https://md.research.elamur.ai/free/en/papers/zhang-2025-iopo-empowering-llms-complex-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinghua Zhang, Haiyang Yu, Cheng Fu, et al.*2025 · 14 citations* 6. **[LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios](https://md.research.elamur.ai/free/en/papers/wu-2025-lifbench-evaluating-instruction-following-performance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Wu, Minhao Wang, Yichen Liu, et al.*2025 · 11 citations* 7. **[MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://md.research.elamur.ai/free/en/papers/liu-2025-mdcure-scalable-pipeline-multi-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.*2025 · 11 citations* 8. **[DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://md.research.elamur.ai/free/en/papers/li-2025-deepsolution-boosting-complex-engineering-solution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuoqun Li, Haiyang Yu, Xuanang Chen, et al.*2025 · 10 citations* 9. **[CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability](https://md.research.elamur.ai/free/en/papers/peng-2025-cafe-retrieval-head-based-coarse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Han Peng, Jinhao Jiang, Zican Dong, et al.*2025 · 4 citations* 10. **[KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation](https://md.research.elamur.ai/free/en/papers/tatarinov-2026-kg-mulqa-framework-kg-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, et al.*2026 · 1 citation* 11. **[LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams](https://md.research.elamur.ai/free/en/papers/wu-2026-livelongbench-tackling-long-context-understanding/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongxuan Wu, Runyu Chen, Peiyu Liu, et al.*2026 · 1 citation* 12. **[An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA](https://md.research.elamur.ai/free/en/papers/sharma-2026-answer-is-just-start-related/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saransh Sharma, Pritika Ramu, Aparna Garimella, et al.*2026* 13. **[Data Foundations of Long-Context Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/sun-2026-data-foundations-long-context-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zechen Sun, Yuyang Sun, Zhaochen Su, et al.*2026* 14. **[Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models](https://md.research.elamur.ai/free/en/papers/molfese-2026-exploring-fine-tuning-context-retrieval/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Francesco Maria Molfese, Momchil Hardalov, Rexhina Blloshmi, et al.*2026* 44 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/run-luo/) — Run Luo