mlsanityData to 14 Oct 2026

Explanova: Automatically Discover Data Insights in N × M Table via XAI Combined LLM Workflow

arXiv 2601.12317 ↗First posted 18 Jan 2026cs.LG cs.AIcited by 0

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

Abstract (arXiv)

Automation in data analysis has been a long-time pursuit. Current agentic LLM shows a promising solution towards it. Like DeepAnalyze, DataSage, and Datawise. They are all powerful agentic frameworks for automatic fine-grained analysis and are powered by LLM-based agentic tool calling ability. However, what about powered by a preset AutoML-like workflow? If we traverse all possible exploration, like Xn itself`s statistics, Xn1-Xn2 relationships, Xn to all other, and finally explain? Our Explanova is such an attempt: Cheaper due to a Local Small LLM.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 17 references that are in the map. First posted on arXiv 18 Jan 2026.

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8 of 17 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025DeepAnalyze: Agentic Large Language Models for Autonomous Data Science19 Oct
  2. 2024InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks10 Jan

Other references · 6

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
  4. 2020Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regressioncited by 154
  5. 2020AutoML-Zero: Evolving Machine Learning Algorithms From Scratchcited by 143
  6. 2016HDBSCAN: Density based Clustering over Location Based Servicescited by 5

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