mlsanityData to 7 Oct 2026

LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data

arXiv 2507.13413 ↗First posted 17 Jul 2025cs.LGcited by 1

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

AutoML has advanced in handling complex tasks using the integration of LLMs, yet its efficiency remains limited by dependence on specific underlying tools. In this paper, we introduce LightAutoDS-Tab, a multi-AutoML agentic system for tasks with tabular data, which combines an LLM-based code generation with several AutoML tools. Our approach improves the flexibility and robustness of pipeline design, outperforming state-of-the-art open-source solutions on several data science tasks from Kaggle. The code of LightAutoDS-Tab is available in the open repository https://github.com/sb-ai-lab/LADS

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 32 references that are in the map. First posted on arXiv 17 Jul 2025.

Builds on

14 of 32 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025MLZero: A Multi-Agent System for End-to-end Machine Learning Automation20 May
  2. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation5 Oct

Other references · 12

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
  3. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  4. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 368
  5. 2025AIDE: AI-Driven Exploration in the Space of Codecited by 206
  6. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 125
  7. 2024AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitionscited by 70
  8. 2023MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Taskscited by 66
  9. 2021LightAutoML: AutoML Solution for a Large Financial Services Ecosystemcited by 32
  10. 2023TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applicationscited by 32
  11. 2021Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelinescited by 13
  12. 2025GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecturecited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelinescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025TAGAL: Tabular Data Generation using Agentic LLM Methods
  2. 2026TML-Bench: Benchmark for Data Science Agents on Tabular ML Tasks
  3. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
  4. 2024AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
  5. 2026ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
  6. 2025Dataforge: Agentic Platform for Autonomous Data Engineering
  7. 2025AutoData: A Multi-Agent System for Open Web Data Collection
  8. 2026TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning