{
  "id": 4449,
  "url": "https://arxiv.org/abs/2605.12376v2",
  "title": "ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows",
  "summary": "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 framew",
  "authors": "Wei Liu, Yang Gu, Xi Yan, Zihan Nan, Beicheng Xu, Keyao Ding et al.",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-12T16:42:38.000Z",
  "fetched_at": "2026-07-14T16:30:59.238Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4449",
  "original_url": "https://arxiv.org/abs/2605.12376v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}