@article{jin2026reasoning,title={Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety},author={Jin, Can and Wu, Rui and Che, Tong and Zhang, Qixin and Peng, Hongwu and Zhao, Jiahui and Wang, Zhenting and Wei, Wenqi and Han, Ligong and Zhang, Zhao and Cao, Yuan and Tang, Ruixiang and Metaxas, Dimitris N.},journal={arXiv preprint arXiv:2601.08000},year={2026},}
@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},}
@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
@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},}
@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},}
@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},}
@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},}
@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
@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},}
@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
@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},}
@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},}