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 2026 (WWW'26)},year={2026},url={https://dl.acm.org/doi/10.1145/3774904.3792995}}
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 43rd International Conference on Machine Learning (ICML 2026)},year={2026},url={https://icml.cc/virtual/2026/poster/64878}}