ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows

TL;DR

ProfiliTable is an autonomous multi-agent framework that enhances tabular data processing by dynamic profiling, iterative refinement, and interactive exploration, improving robustness and accuracy in complex data transformation tasks.

Contribution

It introduces a novel multi-agent system with dynamic profiling and feedback-driven refinement for more reliable and semantically accurate table data processing.

Findings

Outperforms strong baselines on 18 tabular task types

Effective in complex multi-step data transformation scenarios

Dynamic profiling improves semantic understanding and robustness

Abstract

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.