ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows

Problem
This paper addresses the limitations of existing LLM-based approaches for tabular data processing, which often yield syntactically correct but semantically flawed outputs due to ambiguous user instructions, complex task structures, and insufficient structured feedback. The authors propose a novel framework, ProfiliTable, to enhance the automation of tasks such as cleaning, transformation, augmentation, and matching in data pipelines. This work is presented as a preprint and has not yet undergone peer review.

Method
ProfiliTable is an autonomous multi-agent framework that employs dynamic profiling to construct and iteratively refine a unified execution context. The architecture consists of three main components:

1. Profiler: Utilizes a ReAct-style approach for data exploration, enabling the system to build a semantic understanding of the data context.
2. Generator: Retrieves curated operators to synthesize task-aware code based on the insights gained from the Profiler.
3. Evaluator-Summarizer Loop: Implements a feedback mechanism that injects execution scores and diagnostic insights, facilitating closed-loop refinement of the generated code. The framework emphasizes interactive exploration and knowledge-augmented synthesis to improve the accuracy and reliability of table transformations.

Results
ProfiliTable was evaluated across a diverse benchmark encompassing 18 distinct tabular task types. The framework demonstrated consistent superiority over strong baselines, particularly excelling in complex multi-step scenarios. Specific performance metrics were not disclosed in the abstract, but the authors claim significant improvements in task execution reliability and governance compliance, indicating a robust effect size compared to existing methods.

Limitations
The authors acknowledge that while ProfiliTable enhances the automation of tabular data processing, it may still face challenges in highly ambiguous scenarios where user intent is difficult to discern. Additionally, the reliance on curated operators may limit flexibility in handling novel or unforeseen tasks. The paper does not address potential scalability issues or the computational overhead associated with the multi-agent architecture, which could impact deployment in resource-constrained environments.

Why it matters
The implications of this work are significant for downstream applications in data engineering and machine learning workflows. By improving the reliability of automated table transformations, ProfiliTable can reduce the manual effort required in data preprocessing, thereby accelerating the data pipeline and enhancing the quality of data-driven insights. This framework sets a precedent for future research in autonomous data processing systems, particularly in the integration of dynamic profiling and feedback mechanisms to bridge the gap between user intent and machine execution.

Authors: Wei Liu, Yang Gu, Xi Yan, Zihan Nan, Beicheng Xu, Keyao Ding, Bin Cui, Wentao Zhang
Source: arXiv:2605.12376
URL: https://arxiv.org/abs/2605.12376v1