ACT: Agentic Classification Tree

方法与贡献

通过基于不纯度的划分选择与 TextGrad 优化，学习由自然语言二元问题组成的分类树。

后续以智能体方式学习问题决策树的工作，将问题优化与树结构学习结合。

结合主题阅读

大语言模型决策树：从 Tree Prompting 到智能体分类 →

引用

Vincent Grari, Tim Arni, Thibault Laugel, Sylvain Lamprier, James Zou, Marcin Detyniecki. ACT: Agentic Classification Tree. arXiv:2509.26433.

@misc{act,
  title = {{ACT: Agentic Classification Tree}},
  author = {Grari, Vincent and Arni, Tim and Laugel, Thibault and Lamprier, Sylvain and Zou, James and Detyniecki, Marcin},
  year = {2025},
  url = {https://arxiv.org/html/2509.26433v4},
  eprint = {2509.26433},
  archivePrefix = {arXiv}
}