mlsanityData to 7 Oct 2026

AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

arXiv 2410.20424 ↗First posted 27 Oct 2024cs.AI cs.CLcited by 70

Not on the map. Why?

Cited by 47 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system. AutoKaggle implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency. The framework offers highly customizable workflows, allowing users to intervene at each phase, thus integrating automated intelligence with human expertise. Our universal data science toolkit, comprising validated functions for data cleaning, feature engineering, and modeling, forms the foundation of this solution, enhancing productivity by streamlining common tasks. We selected 8 Kaggle competitions to simulate data processing workflows in real-world application scenarios. Evaluation results demonstrate that AutoKaggle achieves a validation submission rate of 0.85 and a comprehensive score of 0.82 in typical data science pipelines, fully proving its effectiveness and practicality in handling complex data science tasks.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 27 Oct 2024.

Builds on

13 of 30 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  5. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  6. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  7. 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineeringcited by 429
  8. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 329
  9. 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 202
  10. 2024DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoningcited by 119
  11. 2023MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Taskscited by 66
  12. 2023ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Codecited by 63
1 more
  1. 2024PyBench: Evaluating LLM Agent on various real-world coding taskscited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 45; 2026: 22.

70 papers.

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  1. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  2. 2025Multi-agent Architecture Search via Agentic Supernetcited by 164
  3. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  4. 2025MLGym: A New Framework and Benchmark for Advancing AI Research Agentscited by 111
  5. 2025AgentRxiv: Towards Collaborative Autonomous Researchcited by 64
  6. 2025LLM Agents for Education: Advances and Applicationscited by 59
  7. 2025ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoningcited by 57
  8. 2025MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinementcited by 52
  9. 2025EvoFlow: Evolving Diverse Agentic Workflows On The Flycited by 49
  10. 2024DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaborationcited by 40
  11. 2025MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineeringcited by 38
  12. 2025ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineeringcited by 26
38 more
  1. 2025Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflowscited by 23
  2. 2024Evaluating and Aligning CodeLLMs on Human Preferencecited by 22
  3. 2024A Survey on Large Language Model-based Agents for Statistics and Data Sciencecited by 22
  4. 2025MLZero: A Multi-Agent System for End-to-end Machine Learning Automationcited by 22
  5. 2025Large Language Model-based Data Science Agent: A Surveycited by 22
  6. 2025SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Mannercited by 18
  7. 2025AutoMLGen: Navigating Fine-Grained Optimization for Coding Agentscited by 17
  8. 2025LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Sciencecited by 15
  9. 2026ResearchGym: Evaluating Language Model Agents on Real-World AI Researchcited by 15
  10. 2025I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Searchcited by 14
  11. 2026DSGym: A Holistic Framework for Evaluating and Training Data Science Agentscited by 14
  12. 2025GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIScited by 13
  13. 2025Reinforcement Learning for Machine Learning Engineering Agentscited by 13
  14. 2025Scaling Generalist Data-Analytic Agentscited by 11
  15. 2025A Survey of Scaling in Large Language Model Reasoningcited by 10
  16. 2025IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Expertscited by 7
  17. 2025SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Sciencecited by 7
  18. 2025BioDSA-1K: Benchmarking Data Science Agents for Biomedical Researchcited by 7
  19. 2025AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Sciencecited by 7
  20. 2025SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Modelscited by 7
  21. 2025KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problemscited by 7
  22. 2026AI Research Preference Modelscited by 7
  23. 2025Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Studycited by 6
  24. 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gapcited by 6
  25. 2025CoMind: Towards Community-Driven Agents for Machine Learning Engineeringcited by 5
  26. 2025IFEvalCode: Controlled Code Generationcited by 5
  27. 2025Select-Then-Decompose: From Empirical Analysis to Adaptive Selection Strategy for Task Decomposition in Large Language Modelscited by 5
  28. 2025Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Callingcited by 4
  29. 2025Agint: Agentic Graph Compilation for Software Engineering Agentscited by 4
  30. 2025KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classificationcited by 3
  31. 2025Can Agentic AI Match the Performance of Human Data Scientists?cited by 3
  32. 2026Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Designcited by 3
  33. 2025Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboardcited by 2
  34. 2025AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Researchcited by 2
  35. 2026DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learningcited by 2
  36. 2026AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Sciencecited by 2
  37. 2026EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Managementcited by 2
  38. 2026Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineeringcited by 2

50 most cited of 70 All 70 (JSON)