# ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows

arXiv: https://arxiv.org/abs/2605.12376 · HTML: https://mlsanity.com/paper/2605.12376/

## 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 32 of its 66 references that are in the map.
- First posted on arXiv 12 May 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 30 of 32)

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- [LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation](https://mlsanity.com/paper/2410.21520/) (2024)
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- [CleanAgent: Automating Data Standardization with LLM-based Agents](https://mlsanity.com/paper/2403.08291/) (2024)
- [Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection](https://mlsanity.com/paper/2110.14019/) (2021)
- [Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets](https://mlsanity.com/paper/2503.06664/) (2025)
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- [Anomaly Detection of Tabular Data Using LLMs](https://mlsanity.com/paper/2406.16308/) (2024)
- [DSMentor: Enhancing Data Science Agents with Curriculum Learning and Online Knowledge Accumulation](https://mlsanity.com/paper/2505.14163/) (2025)
- [DataGovBench: Benchmarking LLM Agents for Real-World Data Governance Workflows](https://mlsanity.com/paper/2512.04416/) (2025)

## Abstract (arXiv)

Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines. While recent LLM-based approaches show promise for automating such tasks, they often struggle in practice due to ambiguous instructions, complex task structures, and the lack of structured feedback, resulting in syntactically correct but semantically flawed code. To address these challenges, we propose ProfiliTable, an autonomous multi-agent framework centered on dynamic profiling, which constructs and iteratively refines a unified execution context through interactive exploration, knowledge-augmented synthesis, and feedback-driven refinement. ProfiliTable integrates (i) a Profiler that performs ReAct-style data exploration to build semantic understanding, (ii) a Generator that retrieves curated operators to synthesize task-aware code, and (iii) an Evaluator-Summarizer loop that injects execution scores and diagnostic insights to enable closed-loop refinement. Extensive experiments on a diverse benchmark covering 18 tabular task types demonstrate that ProfiliTable consistently outperforms strong baselines, particularly in complex multi-step scenarios. These results highlight the critical role of dynamic profiling in reliably translating ambiguous user intents into robust and governance-compliant table transformations.