arXiv:2605.12376v2 Announce Type: replace 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 constru
The proliferation of LLM-based approaches has highlighted their practical limitations in complex data tasks, necessitating more robust and autonomous solutions to move beyond syntactically correct but semantically flawed results.
This development addresses a critical bottleneck in data pipelines, enabling more reliable and automated data processing, which is foundational for AI applications and institutional intelligence.
The shift towards profiling-driven, autonomous multi-agent frameworks for tabular data processing reduces human intervention and improves the semantic accuracy of transformed data.
- · Data scientists
- · AI-driven enterprises
- · Data analytics platforms
- · Automation software providers
- · Manual data cleaning services
- · Legacy ETL tool providers
- · Companies with poor data governance
Improved efficiency and reliability in data-intensive workflows across industries.
Accelerated development and deployment of AI models due to higher quality training data.
Reduced operational costs and increased competitive advantage for businesses that adopt advanced data processing agents.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI