Can Jin

I am a Ph.D. Candidate in Computer Science at Rutgers University, New Brunswick, advised by Professor Dimitris N. Metaxas. My research interests include Pre-training/Post-training/Inference of Large Foundation Models, Efficient AI, and AI Agents.

I earned my M.S. and B.S. degrees in Mathematics from the University of Science and Technology of China (USTC). Before my doctoral studies, I worked as a Machine Learning Engineer at Meituan Dianping Corporation, developing forecasting models for logistics and supply chain management. Recently, as a Research Intern at Adobe Research, I focused on the efficient pre-training of large foundation models (LLMs and DiTs) via dynamic routing Mixture-of-Experts (MoE).

I am open to collaboration on related projects. Please feel free to reach out via email if you share similar interests.

I am actively seeking research internship opportunities for Summer 2026, focusing on Pre-training/Post-training/Inference of Large Foundation Models, Efficient AI, and AI Agents. You can find my CV here.

Research

Large Foundation Models Pre-training, Post-training, and Inference

Focus: Enhancing the effectiveness of pre-training and post-training for generalization and reasoning capabilities.
Approach: Investigating advanced training techniques including Mixture-of-Experts, supervised fine-tuning, reinforcement learning, and inference-time scaling methods such as self-refinement and tree search.
Outcomes: NeurIPS 2024, WWW 2025 (RelWeb), arXiv 2025.

Efficient AI

Focus: Improving the efficiency of models’ training and inference.
Approach: Exploring methods such as model distillation, pruning, and prompting etc.
Outcomes: ICLR 2025, ICML 2025, NeurIPS 2025, AAAI 2025, and WWW 2025 (RelWeb).

AI Agents

Focus: Enhancing the performance of AI agents in multi-agent systems.
Approach: Utilizing reinforcement learning, supervised fine-tuning, and AI data generation pipelines.
Outcomes: NeurIPS 2025 (SEA), arXiv 2025.

Academic Services

Teaching Assistant

Rutgers University:
- CS534: Computer Vision (Spring 2025)
- CS210: Data Management for Data Science (Fall 2024)
- CS211: Computer Architecture (Fall 2025)

Peer Review

Conference: NeurIPS 2025, CVPR 2025/2026, ICLR 2025, AAAI 2026, ICML 2024
Journal: Alexandria Engineering Journal, Information Fusion, Pattern Recognition, Signal Processing

selected publications

arXiv

Sparsity-Controllable Dynamic Top-p MoE for Large Foundation Model Pre-training

Can Jin^*, Hongwu Peng^*, Mingcan Xiang, Qixin Zhang, Xiangchi Yuan, Amit Hasan, Ohiremen Dibua, Yifan Gong, Yan Kang^†, and Dimitris N. Metaxas^†

arXiv preprint arXiv:2512.13996, 2025

Bib PDF

@article{jin2025sparsity,
  title = {Sparsity-Controllable Dynamic Top-p MoE for Large Foundation Model Pre-training},
  author = {Jin, Can and Peng, Hongwu and Xiang, Mingcan and Zhang, Qixin and Yuan, Xiangchi and Hasan, Amit and Dibua, Ohiremen and Gong, Yifan and Kang, Yan and Metaxas, Dimitris N.},
  journal = {arXiv preprint arXiv:2512.13996},
  year = {2025},
}

arXiv

Your reward function for RL is your best PRM for search: Unifying RL and search-based TTS

Can Jin, Yang Zhou, Qixin Zhang, Hongwu Peng , Di Zhang, Marco Pavone, Ligong Han, ZhangWei Hong, Tong Che^†, and Dimitris N Metaxas^†

arXiv preprint arXiv:2508.14313, 2025

Bib PDF

@article{jin2025your,
  title = {Your reward function for RL is your best PRM for search: Unifying RL and search-based TTS},
  author = {Jin, Can and Zhou, Yang and Zhang, Qixin and Peng, Hongwu and Zhang, Di and Pavone, Marco and Han, Ligong and Hong, ZhangWei and Che, Tong and Metaxas, Dimitris N},
  journal = {arXiv preprint arXiv:2508.14313},
  year = {2025},
}

NeurIPS-SEA

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Can Jin, Hongwu Peng, Qixin Zhang, Yujin Tang, Tong Che^†, and Dimitris N Metaxas^†

In Workshop on Scaling Environments for Agents, 2025

Bib PDF Code Poster

@inproceedings{jin2025two,
  title = {Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning},
  author = {Jin, Can and Peng, Hongwu and Zhang, Qixin and Tang, Yujin and Che, Tong and Metaxas, Dimitris N},
  booktitle = {Workshop on Scaling Environments for Agents},
  year = {2025},
}

NeurIPS

Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate

Can Jin^*, Tong Che^*, Hongwu Peng^†, Yiyuan Li^†, Dimitris Metaxas^‡, and Marco Pavone^‡

In Advances in Neural Information Processing Systems, 2024

Bib PDF Code Poster Website

@inproceedings{jin2024lot,
  author = {Jin, Can and Che, Tong and Peng, Hongwu and Li, Yiyuan and Metaxas, Dimitris and Pavone, Marco},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Globerson, A. and Mackey, L. and Belgrave, D. and Fan, A. and Paquet, U. and Tomczak, J. and Zhang, C.},
  pages = {966--994},
  publisher = {Curran Associates, Inc.},
  title = {Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate},
  url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/01ce1ae7f94d139e4917f9e4425a4f38-Paper-Conference.pdf},
  volume = {37},
  year = {2024},
}

ICLR

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

Can Jin , Ying Li , Mingyu Zhao, Shiyu Zhao, Zhenting Wang, Xiaoxiao He, Ligong Han, Tong Che, and Dimitris N. Metaxas

In The Thirteenth International Conference on Learning Representations, 2025

Bib PDF Code

@inproceedings{jin2025lorvp,
  title = {LoR-{VP}: Low-Rank Visual Prompting for Efficient Vision Model Adaptation},
  author = {Jin, Can and Li, Ying and Zhao, Mingyu and Zhao, Shiyu and Wang, Zhenting and He, Xiaoxiao and Han, Ligong and Che, Tong and Metaxas, Dimitris N.},
  booktitle = {The Thirteenth International Conference on Learning Representations},
  year = {2025},
  url = {https://openreview.net/forum?id=5btFIv2PNb},
}

AAAI

Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective

Can Jin^*, Tianjin Huang^* , Yihua Zhang, Mykola Pechenizkiy, Sijia Liu , Shiwei Liu, and Tianlong Chen

In Proceedings of the AAAI Conference on Artificial Intelligence, 2025

Bib PDF Code

@inproceedings{jin2025visual,
  title = {Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective},
  author = {Jin, Can and Huang, Tianjin and Zhang, Yihua and Pechenizkiy, Mykola and Liu, Sijia and Liu, Shiwei and Chen, Tianlong},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {39},
  number = {4},
  pages = {4111--4119},
  year = {2025},
  url = {https://arxiv.org/pdf/2312.01397},
}

WWW

APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking (Best Paper Award @ RelWeb)

Can Jin^*, Hongwu Peng^*, Shiyu Zhao, Zhenting Wang, Wujiang Xu, Ligong Han , Jiahui Zhao, Kai Zhong, Sanguthevar Rajasekaran, and Dimitris N Metaxas

In Companion Proceedings of the ACM Web Conference 2025, Sydney, NSW, Australia, 2025

DOI Bib PDF Code

@inproceedings{jin2025apeer,
  title = {APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking (<span class="award">Best Paper Award @ RelWeb</span>)},
  author = {Jin, Can and Peng, Hongwu and Zhao, Shiyu and Wang, Zhenting and Xu, Wujiang and Han, Ligong and Zhao, Jiahui and Zhong, Kai and Rajasekaran, Sanguthevar and Metaxas, Dimitris N},
  booktitle = {Companion Proceedings of the ACM Web Conference 2025},
  location = {Sydney, NSW, Australia},
  series = {WWW '25},
  year = {2025},
  doi = {10.1145/3701716.3717574},
  isbn = {979-8-4007-1331-6/2025/04},
  url = {https://arxiv.org/abs/2406.14449},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  keywords = {Prompt engineering, Information Retrieval, Large Language Model, ReRanking},
}

WWW

RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models

Can Jin^*, Hongwu Peng^* , Anxiang Zhang , Nuo Chen , Jiahui Zhao, Xi Xie , Kuangzheng Li, Shuya Feng, Kai Zhong, Caiwen Ding, and Dimitris N Metaxas

In Companion Proceedings of the ACM Web Conference 2025, Sydney, NSW, Australia, 2025

DOI Bib PDF Code

@inproceedings{jin2025rankflow,
  title = {RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models},
  author = {Jin, Can and Peng, Hongwu and Zhang, Anxiang and Chen, Nuo and Zhao, Jiahui and Xie, Xi and Li, Kuangzheng and Feng, Shuya and Zhong, Kai and Ding, Caiwen and Metaxas, Dimitris N},
  booktitle = {Companion Proceedings of the ACM Web Conference 2025},
  location = {Sydney, NSW, Australia},
  series = {WWW '25},
  year = {2025},
  doi = {10.1145/3701716.3717575},
  isbn = {979-8-4007-1331-6/2025/04},
  url = {https://arxiv.org/abs/2502.00709},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  keywords = {Information Retrieval, Large Language Model, ReRanking},
}

ACM MM

Graph Canvas for Controllable 3D Scene Generation

Libin Liu , Shen Chen, Sen Jia, Jingzhe Shi, Can Jin, Zongkai Wu, Jenq-Neng Hwang , and Lei Li

In Proceedings of the 33rd ACM International Conference on Multimedia, Dublin, Ireland, 2025

DOI Bib PDF

@inproceedings{liu2025graph,
  author = {Liu, Libin and Chen, Shen and Jia, Sen and Shi, Jingzhe and Jin, Can and Wu, Zongkai and Hwang, Jenq-Neng and Li, Lei},
  title = {Graph Canvas for Controllable 3D Scene Generation},
  year = {2025},
  isbn = {9798400720352},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3746027.3754554},
  doi = {10.1145/3746027.3754554},
  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
  pages = {2536–2545},
  numpages = {10},
  keywords = {3d generation, graph representation, multimodal large language model, spatial intelligence},
  location = {Dublin, Ireland},
  series = {MM '25},
}

NeurIPS

Effective Policy Learning for Multi-Agent Online Coordination Beyond Submodular Objectives

Qixin Zhang, Yan Sun, Can Jin , Xikun ZHANG, Yao Shu , Puning Zhao, Li Shen, and Dacheng Tao

In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025

Bib PDF Poster

@inproceedings{zhang2025effective,
  title = {Effective Policy Learning for Multi-Agent Online Coordination Beyond Submodular Objectives},
  author = {Zhang, Qixin and Sun, Yan and Jin, Can and ZHANG, Xikun and Shu, Yao and Zhao, Puning and Shen, Li and Tao, Dacheng},
  booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year = {2025},
  url = {https://openreview.net/forum?id=isAYKdLwtB},
}

ICML

Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints

Qixin Zhang , Wei Huang, Can Jin , Puning Zhao, Yao Shu, Li Shen, and Dacheng Tao

In Forty-second International Conference on Machine Learning, 2025

Bib PDF Poster

@inproceedings{zhang2025multinoulli,
  title = {Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints},
  author = {Zhang, Qixin and Huang, Wei and Jin, Can and Zhao, Puning and Shu, Yao and Shen, Li and Tao, Dacheng},
  booktitle = {Forty-second International Conference on Machine Learning},
  year = {2025},
  url = {https://openreview.net/forum?id=6XQOarhYF8},
}