mlsanityData to 14 Oct 2026

ACT: Agentic Classification Tree

arXiv 2509.26433 ↗First posted 30 Sep 2025cs.LG cs.AIcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 32 of its 59 references that are in the map. First posted on arXiv 30 Sep 2025.

Builds on

32 of 59 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  2. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors21 Aug

Other references · 30

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  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2025Qwen3 Technical Reportcited by 8,820
  6. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  7. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  8. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  9. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  10. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  11. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  12. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
18 more
  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  2. 2025Gemma 3 Technical Reportcited by 1,824
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  5. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  6. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 886
  7. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  8. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 584
  9. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 489
  10. 2024TextGrad: Automatic "Differentiation" via Textcited by 273
  11. 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 160
  12. 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 138
  13. 2020Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challengescited by 120
  14. 2019The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanationscited by 97
  15. 2022DDXPlus: A New Dataset For Automatic Medical Diagnosiscited by 88
  16. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  17. 2019Assessing the Local Interpretability of Machine Learning Modelscited by 23
  18. 2025Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Human-AI Co-design for Clinical Prediction Modelscited by 9

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