Pengxiang LI

@article{li2025efficient,
  title = {Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation},
  author = {Li, Pengxiang and Hu, Zechen and Shang, Zirui and Wu, Jingrong and Liu, Yang and Liu, Hui and Gao, Zhi and Shi, Chenrui and Zhang, Bofei and Zhang, Zihao and Shi, Xiaochuan and Yu, Zedong and Wu, Yuwei and Wu, Xinxiao and Jia, Yunde and Xiang, Liuyu and He, Zhaofeng and Li, Qing},
  journal = {arXiv preprint arXiv:2509.23866},
  year = {2025},
  correspondence = {Gao, Zhi and Li, Qing and Wu, Yuwei},
}

@article{wang2025objects,
  title = {From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes},
  author = {Wang, Tianxu and Zhang, Zhuofan and Zhu, Ziyu and Fan, Yue and Xiong, Jing and Li, Pengxiang and Ma, Xiaojian and Li, Qing},
  journal = {Neural Information Processing Systems: Datasets and Benchmarks (NeurIPS D&B)},
  year = {2025},
  correspondence = {Li, Qing},
}

@article{zhang2025chainoffocusadaptivevisualsearch,
  title = {Chain-of-Focus: Adaptive Visual Search and Zooming for Multimodal Reasoning via RL},
  author = {Zhang, Xintong and Gao, Zhi and Zhang, Bofei and Li, Pengxiang and Zhang, Xiaowen and Liu, Yang and Yuan, Tao and Wu, Yuwei and Jia, Yunde and Zhu, Song-Chun and Li, Qing},
  equalauthor = {Zhang, Xintong and Gao, Zhi},
  year = {2025},
  correspondence = {Wu, Yuwei and Li, Qing},
}

@article{2025iterative,
  title = {Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning },
  author = {Li, Pengxiang and Gao, Zhi and Zhang, Bofei and Mi, Yapeng and Ma, Xiaojian and Shi, Chenrui and Yuan, Tao and Wu, Yuwei and Jia, Yunde and Zhu, Song-Chun and Li, Qing},
  equalauthor = { Li, Pengxiang and Gao, Zhi},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  correspondence = {Wu, Yuwei and Li, Qing},
}

@article{2025mat,
  title = {Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage},
  author = {Gao, Zhi and Zhang, Bofei and Li, Pengxiang and Ma, Xiaojian and Yuan, Tao and Fan, Yue and Wu, Yuwei and Jia, Yunde and Zhu, Song-Chun and Li, Qing},
  equalauthor = {Gao, Zhi and Zhang, Bofei and Li, Pengxiang},
  correspondence = {Wu, Yuwei and Li, Qing},
  year = {2025},
  journal = {International Conference on Learning Representations (ICLR)},
}

@article{2024sg3d,
  title = {Task-oriented Sequential Grounding in 3D Scenes},
  author = {Zhang, Zhuofan and Zhu, Ziyu and Li, Pengxiang and Liu, Tengyu and Ma, Xiaojian and Chen, Yixin and Jia, Baoxiong and Huang, Siyuan and Li, Qing},
  journal = {arXiv preprint arXiv:2408.04034},
  year = {2024},
  correspondence = {Wu, Yuwei and Li, Qing},
}

@article{2024fire,
  title = {FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models},
  author = {Li, Pengxiang and Gao, Zhi and Zhang, Bofei and Yuan, Tao and Wu, Yuwei and Harandi, Mehrtash and Jia, Yunde and Zhu, Song-Chun and Li, Qing},
  journal = {Neural Information Processing Systems: Datasets and Benchmarks (NeurIPS D&B)},
  year = {2024},
  equalauthor = {Li, Pengxiang and Gao, Zhi and Zhang, Bofei},
  correspondence = {Wu, Yuwei and Li, Qing},
}

@article{2024issga,
  title = {Inter-Scale Similarity Guided Cost Aggregation for Stereo Matching},
  author = {Li, Pengxiang and Yao, Chengtang and Wu, Yuwei and Jia, Yunde},
  journal = {IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)},
  year = {2024},
  correspondence = {Wu, Yuwei},
  paper = {https://ieeexplore.ieee.org/document/10663688},
}

@article{2023hyperbolicTutorial,
  title = {Hyperbolic Learning: Theory and Applications},
  author = {Li, Pengxiang and Yu, Peilin and Xue, Yangkai and Wu, Yuwei and Gao, Zhi},
  year = {2023},
}

@article{yao2021decomposition,
  title = {A decomposition model for stereo matching},
  author = {Yao, Chengtang and Jia, Yunde and Di, Huijun and Li, Pengxiang and Wu, Yuwei},
  journal = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
  correspondence = {Wu, Yuwei},
}