Publications

2026

1. arXiv

   Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning

   Kaiwen Chen^*, Shuhai Zhang^*, and 8 more authors

   arXiv preprint arXiv:2606.14187, 2026

   Abs arXiv Bib PDF Code

   Zeta is a dual whitening optimizer for matrix-aware neural network training. It combines coordinate whitening and spectral whitening to address coordinate-wise scale imbalance in momentum matrices, improving conditioning and orthogonalization reliability across language modeling, MoE, and vision workloads.

   @article{chen2026zeta,
     title = {Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning},
     author = {Chen, Kaiwen and Zhang, Shuhai and Liu, Zimo and Li, Linxiao and Sun, Ying and Li, Yuchen and Zhang, Yifan and Han, Bo and Tan, Mingkui and Chen, Qiuwu},
     journal = {arXiv preprint arXiv:2606.14187},
     year = {2026},
     url = {https://arxiv.org/abs/2606.14187v2}
   }

2. WWW

   EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents

   Linxiao Li^* and Zhixiang Lu^*†

   In Proceedings of the ACM Web Conference (WWW), 2026

   Oral (Selection Rate: 4.6%) Abs Bib PDF

   EcoThink is a green adaptive inference framework that addresses LLM overthinking in agent systems. It achieves an average of 40.4% energy savings (up to 81.9%) without significant performance loss, making AI agents more sustainable and accessible.

   @inproceedings{li2026ecothink,
     title = {EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents},
     author = {Li, Linxiao and Lu, Zhixiang},
     booktitle = {Proceedings of the ACM Web Conference (WWW)},
     year = {2026},
     url = {https://dl.acm.org/doi/10.1145/3774904.3792995}
   }

3. ICML

   Deterministic Component Mining for Multi-framework UI2Code Generation

   Zixiong Yang^*, Linxiao Li^*, and 4 more authors

   In Proceedings of the International Conference on Machine Learning (ICML), 2026

   Poster (Acceptance Rate: 26.6%) Abs Bib PDF

   We propose a deterministic component mining approach for multi-framework UI2Code generation, enabling accurate and framework-agnostic code synthesis from UI designs.

   @inproceedings{yang2026dcm,
     title = {Deterministic Component Mining for Multi-framework UI2Code Generation},
     author = {Yang, Zixiong and Li, Linxiao and Lin, Jiaye and Wu, Binrui and Kang, Xiaoyu and Gao, Jiechao},
     booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
     year = {2026},
     url = {https://icml.cc/virtual/2026/poster/64878}
   }