{
  "id": "act",
  "title": "ACT: Agentic Classification Tree",
  "authors": [
    {
      "given": "Vincent",
      "family": "Grari"
    },
    {
      "given": "Tim",
      "family": "Arni"
    },
    {
      "given": "Thibault",
      "family": "Laugel"
    },
    {
      "given": "Sylvain",
      "family": "Lamprier"
    },
    {
      "given": "James",
      "family": "Zou"
    },
    {
      "given": "Marcin",
      "family": "Detyniecki"
    }
  ],
  "year": 2025,
  "venue": "arXiv:2509.26433",
  "paper_url": "https://arxiv.org/html/2509.26433v4",
  "summary": "Learns classification trees of natural-language binary questions through impurity-based split selection and TextGrad refinement.",
  "connection": "A later agentic formulation of learning question-based decision trees; compare question optimization and tree construction.",
  "bibtex_type": "article",
  "citation_key": "act",
  "source_urls": [
    "https://arxiv.org/html/2509.26433v4"
  ],
  "arxiv_id": "2509.26433",
  "arxiv_first_posted": "2025-09-30",
  "publication_type": "preprint",
  "container_title": "arXiv",
  "updated_at": "2026-11-10",
  "record_url": "https://yuntiandeng.com/research/papers/act/"
}