Haonan Wang

Haonan Wang

Johns Hopkins University

🤔 Ongoing Projects

👇 Select Publications

đź“•Large Language Models(LLMs) and Human-Centered AI

TBD
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PerspectiveCoder-LM: A LLM-based Multi-agent System for Large-scale Corpus Inductive Text Coding Analysis
Haonan Wang,Jie Gao, Kristina Gligorić, Ziang Xiao.

Code

EMNLP 2025
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From Noise to Nuance: Enriching Subjective Data Interpretation through Qualitative Analysis
Ruyuan Wan,Haonan Wang,Ting-Hao Kenneth Huang,Jie Gao.

The 4th HCI + NLP Workshop at EMNLP, 2025.

  • Abstract: Subjective data annotation (SDA) in NLP should treat annotator disagreement as a valuable source of insight rather than noise. Drawing on qualitative data analysis (QDA), we compare the two methodologies and highlight differences in human roles, workflows, and evaluation. We propose five recommendations to make SDA more interpretive and demonstrate these ideas through a reinforcement learning from human feedback (RLHF) case study, advocating for a more interdisciplinary approach to understanding subjective data.
EMNLP 2025
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ByteSized32Refactored: Towards an Extensible Interactive Text Games Corpus for LLM World Modeling and Evaluation
Haonan Wang, Junfeng Sun, Xindi Yuan, Ruoyao Wang, Ziang Xiao.

The 5th WordPlay Workshop at EMNLP, 2025. Code Slides Poster-recording

  • Abstract: ByteSized32Refactored presents a modular and extensible redesign of the original ByteSized32 interactive text games corpus to advance research on LLM world modeling and evaluation. By introducing a unified foundation library that abstracts common game logic into reusable base classes, the total codebase is reduced by half while enabling easier expansion to new environments. Experiments with GPT-4o and GPT-5 demonstrate that the refactored structure improves code quality and extensibility while revealing new challenges for hierarchical reasoning in LLMs. This work establishes a scalable platform for studying interactive reasoning, adaptability, and generalization in language models.
Plos one
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Unsupervised Feature Selection Algorithm Based on L2,p-norm Feature Reconstruction
Wei liu, Miao Zhong, Guangwei Liu, Haonan Wang, Qian Ning.

Plos one. Code

  • Abstract: NFRFS (an unsupervised feature selection algorithm based on L2, p-norm feature reconstruction) proposes a more robust feature selection method, aiming to address the problem that traditional algorithms are sensitive to noise and outliers. The L2, p-norm is used to enhance robustness, the integrative adaptive graph learning is employed to maintain the local structure of the data, and the inner product sparse regularization is utilized to select lower-level reference features. Experimental results on 14 benchmark datasets show that NRFFS significantly matches the existing state-of-the-art methods in Bluetooth performance, demonstrating its effectiveness and practicality in high-dimensional data processing.
Plos one
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IM3HRL: Model-assisted Intrinsically Motivated Modular Hierarchical Reinforcement Learning
Wei Liu, Jiaxiang Wang, Guangwei Liu, Haonan Wang.

under review.

  • Abstract: IM3HRL (Model-assisted Internal Reinforcement Hierarchical Reinforcement Learning) is an efficient and robust new reinforcement learning framework specifically designed to address the exploration and sample efficiency issues in complex goal-conditioned tasks. It hierarchically decomposes tasks by combining model-assisted internal guidance (MHA), uses internal guidance (CLP) to direct the agent to prioritize exploration, and employs a model-assisted relabeling strategy (FGRS) to generate reinforcement targets and enhance learning efficiency. Experiments have shown that the learning speed of IM3HRL is at least 15% faster than that of a single reinforcement method, and it demonstrates strong robustness against forgetting and perturbations.

đź“ť All Publications

  • [Dissertation-Project] – Master in Computer Science “Unveiling Statistical Relationships Among Popular LLM Benchmarks: A Quantitative Framework”
    Haonan Wang,Ziang Xiao.
    Johns Hopkins University,2024.[pdf],

  • [Dissertation-Project] – B.S in Information, Computer Science, and Math “Research on the application of human action recognition based on LSTM-CNN”
    Haonan Wang,Wei Liu.
    Liaoning Technology University,2023.[pdf],

  • [Journal] – Advances in AI and ML “Research on geometric figure classification algorithm based on Deep Learning”
    Ruiyang Wang,Haonan Wang,Junfeng Sun,Mingjia Zhao,Meng Liu.
    Advances in Artificial Intelligence and Machine Learning, 2022. [pdf],

  • [Journal] – Scientific Journal “Research status and future prospects of machine learning algorithm in big data analysis”
    Haonan Wang.
    Scientific Journal of Intelligent Systems Research,2021. [pdf],

đź–Ą Software

Software Patents :

—🙏Thanks to the software application development collaboration with all Professors and Graduate Assistants from the SpringTeng AI and Liaoning Technical University, Liaoning University, and Qinghua University, being funded through our collaboration with Chinese central State-owned Shenhua Ming Group Ltd and Zijin Ming Group Ltd enterprises.
[P.1] Haonan Wang, Mingjia, Zhao, et al. (2022). Artificial intelligence robot programming interactive control system. PRC Software Copyright Patent, Patent No. 2022SR1053901.
[P.2] Haonan Wang, Junfeng, Sun, et al. (2022). Image recognition processing operation platform. PRC Software Copyright Patent, Patent No. 2022SR1052419.
[P.3] Haonan Wang, Chang, Liu, et al. (2022). Artificial Intelligence Community Security Equipment Monitoring System. PRC Software Copyright Patent, Patent No. 2022SR1052492.
[P.4] Haonan Wang, Meng, Liu, et al. (2022). A network behavior analysis system based on machine learning. PRC Software Copyright Patent, Patent No. 2022SR1049807.
[P.5] Haonan Wang, Chi, Li, et al. (2022). Autonomous Driving Intelligent Dispatching Center Management System. PRC Software Copyright Patent, Patent No. 2022SR1052526.
[P.6] Haonan Wang, Ruiyang, Wang, et al. (2022). Unmanned shortest path planning system. PRC Software Copyright Patent, Patent No.2022SR0935020.
[P.7] Haonan Wang, Junfeng Sun, et al. (2022). Data operation analysis and collection system based on machine learning. PRC Software Copyright Patent, Patent No. 2022SR1052428.
[P.8] Jiawei, Zhang, Pengyu, Cai, Haonan Wang, et al. (2021). Staff check-in face recognition system.PRC Software Copyright. PRC Software Copyright Patent, Patent No. 2021SR0699354.

Competition Awards(Mathematical Modeling and Computer Science Design):

• 1st Place, 12th Mathor Cup College Mathematical Modeling Challenge 2022
National-level award in China
• 1st Place, Liaoning Mathematical Modeling Contest 2022
Provincial-level award in China
• 1st Place, 7th Shuwei Mathematical Modeling Challenge for College Students 2022
National-level award in China
• 1st Place, 12th Mathor Cup College Mathematical Modeling Challenge 2022
National-level award in China
• 3rd Place, Liaoning Province “Shuo Ri Cup” College Student Computer Design 2022
Provincial-level award in China
• 3rd Place, Northeast Three Provinces Mathematical Modeling Competition 2022
Provincial-level award in China
• 2nd Place, American Mathematical Contest in Modeling 2022
International award
• 3rd Place, 14th National Undergraduate Computer Design Competition 2021
National-level award in China
• 3rd Place, 11th Mathor Cup University Mathematical Modeling Challenge 2021
National-level award in China
• 2nd Place, National College Students “Hua Shu Cup” Mathematical Modeling 2021
National-level award in China
• 1st Place, Liaoning Province “Shuo Ri Cup” College Student Computer Design 2021
Provincial-level award in China,
• 1st Place, Liaoning AgricuLNTUral Economic Modeling Competition 2021
Provincial-level award in China
• 1st Place, Outstanding Scholarship of the Faculty of Science, LNTU 2021
School-level award in China
• 1st Place, Career Planning Competition of the Faculty of Science, LNTU 2021
School-level award in China
• Academic Achievement Award of the School of Science, LNTU 2021
School-level award in China
• 2nd Place, Liaoning Mathematical Modeling Contest 2021
Provincial-level award for China