[2509.26433] ACT: Agentic Classification Tree

Computer Science > Machine Learning arXiv:2509.26433 (cs) [Submitted on 30 Sep 2025 (v1), last revised 5 Apr 2026 (this version, v4)] Title:ACT: Agentic Classification Tree Authors:Vincent Grari, Tim Arni, Thibault Laugel, Sylvain Lamprier, James Zou, Marcin Detyniecki View a PDF of the paper titled ACT: Agentic Classification Tree, by Vincent Grari and 5 other authors View PDF HTML (experimental) Abstract:When used in high-stakes settings, AI systems are expected to produce decisions that are transparent, interpretable and auditable, a requirement increasingly expected by regulations. Decision trees such as CART provide clear and verifiable rules, but they are restricted to structured tabular data and cannot operate directly on unstructured inputs such as text. In practice, large language models (LLMs) are widely used for such data, yet prompting strategies such as chain-of-thought or prompt optimization still rely on free-form reasoning, limiting their ability to ensure trustworthy behaviors. We present the Agentic Classification Tree (ACT), which extends decision-tree methodology to unstructured inputs by formulating each split as a natural-language question, refined through impurity-based evaluation and LLM feedback via TextGrad. Experiments on text benchmarks show that ACT matches or surpasses prompting-based baselines while producing transparent and interpretable decision paths. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: ar...