Jiabin Luo

Peking University PhD. Peking University (2025)

I am Jiabin Luo(罗嘉滨). My research interests are AI systems, LLM, Trustworthy AI, RAG, Multimodal Learning. I am also interested in generative data research. In 2025, I will start my PhD studies at Peking University. I publish a lot of open source projects on 🔗Github GitHub User's stars.Colleagues interested in collaboration, I encourage you to get in touch. I do my best to respond to emails promptly, but please bear with me if there's a slight delay as I check my inbox in time. Your understanding is sincerely appreciated. Thank you!

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🏫Education
  • Peking University

    Peking University

    Ph.D. in Electronic information (AI) Sep. 2025 - Jun. 2030

  • Northeastern University

    Northeastern University

    B.S. in Computer Science (AI) Sep. 2021 - Jun. 2025

🏆Honors & Awards
  • The National Encouragement Scholarship(Top 4% Nationwide)🚩 2024
  • First Prize of The Final of The 15th Chinese Mathematics Competition🥇 2024
  • The Meritorious Winner of The Mathematical Contest in Modeling (MCM)🥈 2024
  • First Prize of The Final of The 14th Chinese Mathematics Competition🥇 2023
  • Second Prize of The China Undergraduate Mathematical Contest in Modeling🥈 2023
  • “Merit Student”, School of Mathematics and Statistics 2023
💻Experience
  • Zhipu AI

    Zhipu AI

    Reaserch Intern Nov. 2024 - Jan. 2025

  • Shanghai AILab

    Shanghai AILab

    Research Intern Feb. 2025 - May. 2025

📚Service
  • Reviewer of IJCNN2024
  • Reviewer of AAAI2026
🔥News
2025
🎉🎉I will serve as the Program Committee for AAAI 2026!
Aug 10
2024
First Human Settlement Established on Mars, Marking New Era of Space Exploration. Read more
Nov 30
🎉🎉Our Paper “Coal Mine Safety Alert System: Refining BP Neural Network with Genetic Algorithm Optimization” was accepted by ICIC2024 (CCF-C)!
Jun 21
The Mathematical Contest in Modeling(MCM): Meritorious Winner🥈(Top 7%)
May 10
The Final of The 15th Chinese Mathematics Competition, Shandong University: First Prize🥇(Top 0.09%) Featured
Apr 25
2023
The Final of The 14th Chinese Mathematics Competition, Guangdong University of Technology: First Prize🥇(Top 0.09%) Featured
Jun 01
2021
At Northeastern University, I began my college life!✌(>‿◠)✌
Aug 31
🗺️Selected Publications (view all )
aiXiv：A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
aiXiv：A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan, Xinyu Liu

Under review. 2025

Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of highquality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of highquality AI-generated research content.

[Paper]

aiXiv：A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
aiXiv：A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan, Xinyu Liu

Under review. 2025

Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of highquality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of highquality AI-generated research content.

[Paper]

Joint deep reinforcement learning strategy in MEC for smart internet of vehicles edge computing networks
Joint deep reinforcement learning strategy in MEC for smart internet of vehicles edge computing networks

Jiabin Luo, Qinyu Song, Fusen Guo, Haoyuan Wu

Sustainable Computing: Informatics and Systems 2025 Journal(SCI Q1, Impact Factor:5.7)Full Paper

The Internet of Vehicles (IoV) has a limited computing capacity, making processing computation tasks challenging. These vehicular services are updated through communication and computing platforms. Edge computing is deployed closest to the terminals to extend the cloud computing facilities. However, the limitation of the vehicular edge nodes, satisfying the Quality of Experience (QoE) is the challenge. This paper developed an imaginative IoV scenario supported by mobile edge computing (MEC) by constructing collaborative processes such as task offloading decisions and resource allocation in various roadside units (RSU) environments that cover multiple vehicles. After that, Deep reinforcement Learning (DRL) is employed to solve the joint optimisation issue. Based on this joint optimisation model, the offloading decisions and resource allocations are gained to reduce the cost obtained in end-to-end delay and expense of resource computation. This problem is formulated based on the Markov Decision Process (MDP) designed functions like state, action, and reward. The proposed model's performance evaluations and numerical results achieve less average delay for 30 vehicle nodes in simulation.

[Paper]

Joint deep reinforcement learning strategy in MEC for smart internet of vehicles edge computing networks
Joint deep reinforcement learning strategy in MEC for smart internet of vehicles edge computing networks

Jiabin Luo, Qinyu Song, Fusen Guo, Haoyuan Wu

Sustainable Computing: Informatics and Systems 2025 Journal(SCI Q1, Impact Factor:5.7)Full Paper

The Internet of Vehicles (IoV) has a limited computing capacity, making processing computation tasks challenging. These vehicular services are updated through communication and computing platforms. Edge computing is deployed closest to the terminals to extend the cloud computing facilities. However, the limitation of the vehicular edge nodes, satisfying the Quality of Experience (QoE) is the challenge. This paper developed an imaginative IoV scenario supported by mobile edge computing (MEC) by constructing collaborative processes such as task offloading decisions and resource allocation in various roadside units (RSU) environments that cover multiple vehicles. After that, Deep reinforcement Learning (DRL) is employed to solve the joint optimisation issue. Based on this joint optimisation model, the offloading decisions and resource allocations are gained to reduce the cost obtained in end-to-end delay and expense of resource computation. This problem is formulated based on the Markov Decision Process (MDP) designed functions like state, action, and reward. The proposed model's performance evaluations and numerical results achieve less average delay for 30 vehicle nodes in simulation.

[Paper]

Coal Mine Safety Alert System： Refining BP Neural Network with Genetic Algorithm Optimization
Coal Mine Safety Alert System： Refining BP Neural Network with Genetic Algorithm Optimization

Jiabin Luo, Hanzhe Pan

International Conference On Intelligent Computing 2024 Conference(CCF-C)Poster

In response to the persistent safety challenges within coal mines, this study proposes a novel approach integrating a three-layer feedforward backpropagation artificial neural network with a genetic algorithm (GA-BP) for establishing a safety early warning system. Focused on a coal mine in Shandong, China, the model's effectiveness is evaluated using relevant data for training and analysisResults indicate the superiority of the GA-BP model over traditional BP neural networks, offering enhanced capability for identifying potential safety risks promptly. This advancement enables coal mine management to implement timely interventions, ensuring the safety of miners. The findings present valuable insights for engineering applications in similar contexts.

[Paper] [Poster]

Coal Mine Safety Alert System： Refining BP Neural Network with Genetic Algorithm Optimization
Coal Mine Safety Alert System： Refining BP Neural Network with Genetic Algorithm Optimization

Jiabin Luo, Hanzhe Pan

International Conference On Intelligent Computing 2024 Conference(CCF-C)Poster

In response to the persistent safety challenges within coal mines, this study proposes a novel approach integrating a three-layer feedforward backpropagation artificial neural network with a genetic algorithm (GA-BP) for establishing a safety early warning system. Focused on a coal mine in Shandong, China, the model's effectiveness is evaluated using relevant data for training and analysisResults indicate the superiority of the GA-BP model over traditional BP neural networks, offering enhanced capability for identifying potential safety risks promptly. This advancement enables coal mine management to implement timely interventions, ensuring the safety of miners. The findings present valuable insights for engineering applications in similar contexts.

[Paper] [Poster]

All Publications