Hongru Sun

dblp:203/2139 · DBLP profile ↗
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4ranked-venue papers
2as first author
2since 2021 · last 2026
0000-0003-2605-0594ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Reinforcement learning · 56% Language models and text generation · 44%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › mathematical reasoning
mathematical problem solving
1.012026
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner · ACL (1) 2026
Machine learning › Reinforcement learning › reinforcement learning for NLP
reinforcement learning for language models
1.012026
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions · ACL (1) 2026
Machine learning › Reinforcement learning › sample efficiency
sample-efficient reinforcement learning
1.012026
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions · ACL (1) 2026
Natural language and speech › Language models and text generation › model steering
language model steering
0.312026
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner · ACL (1) 2026
Natural language and speech › Language models and text generation
large language model training
0.312026
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions · ACL (1) 2026

Methods — techniques the papers use, named apart from their topics

reinforcement learning · 2.0fuzzy logic · 1.0
YearPublicationVenuePosition
2026 FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner
abstract
Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Hao Wu 0094, Hongru Sun, Wanqing Li 0001, Xinguo Yu, Hao Ming 0001, Xiao Luo 0001, Wenbin Zhang 0002, Jiahong Zhao, Yi Guo 0001, Jie Yang 0009
ACL (1)2
2026 A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
abstract
Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo 0002, Chunchun Chen, Yunhui Liu 0002, Hongru Sun, Yatao Bian, Ming Zhang 0004, Tieke He, Jie Yang 0009, Guanjie Zheng, Zhonghai Wu, Bo Zhang 0069, Lei Bai 0020, Xiao Luo 0001
ACL (1)8
2018 Robust Magnetic Resonant Beamforming for Secured Wireless Power Transfer
abstract
Wireless power transfer (WPT) is an emerging and promising technique for power supplies to mobile and portable devices. Among all approaches, magnetic resonant coupling (MRC) is an excellent one for midrange WPT, which provides high mobility, flexibility, and convenience due to its simplicity in hardware implementation and longer transmission distances. In this letter, we consider an MRC-WPT system with multiple power transmitters, one intended power receiver and multiple unintended power receivers. The optimal robust beamforming design of the complex transmit currents is investigated to achieve the minimal total source power with the worst-case mutual inductances measurement, whereas the unintended receiving powers are constrained by certain bounds. Numerical results demonstrate that the proposed algorithm can significantly improve the performance and the robustness of the MRC-WPT systems.
Hongru Sun, Fengchao Zhu, Hai Lin 0001, Feifei Gao 0001
IEEE Signal Process. Lett.1
2017 Magnetic Resonant Beamforming for Secured Wireless Power Transfer
abstract
Magnetic resonance coupling (MRC) has been utilized in wireless power transfer (WPT) to achieve mid-range contactless power supply. However, unintended users might also draw power from the transmission devices. In this letter, an MRC-WPT system with multiple power transmitters, one intended power receiver, and one unintended power receiver is investigated. We formulate a power security problem by limiting the unintended receiving power and, at the same time, maximizing the power of the intended user. Such an optimization problem is in general nonconvex. Nevertheless, a global optimal solution can be efficiently achieved with the aid of semidefinite relaxation approach. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Hongru Sun, Hai Lin 0001, Fengchao Zhu, Feifei Gao 0001
IEEE Signal Process. Lett.1