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[2511.06185] Dataforge: Agentic Platform for Autonomous Data Engineering
Summary
The article presents Dataforge, an LLM-powered platform designed to automate data engineering processes, enhancing efficiency in preparing data for AI applications.
Why It Matters
As AI applications grow, the need for efficient data preparation becomes critical. Dataforge addresses the labor-intensive bottleneck of data cleaning and transformation, making it accessible for non-experts and improving overall AI performance.
Key Takeaways
- Dataforge automates data cleaning and feature optimization, reducing manual effort.
- It operates under a budgeted feedback loop, ensuring efficient resource use.
- The platform achieves superior performance on tabular data benchmarks.
- Iterative refinement and grounding are key to its accuracy and reliability.
- Dataforge represents a significant step towards autonomous data engineering.
Computer Science > Artificial Intelligence arXiv:2511.06185 (cs) [Submitted on 9 Nov 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Dataforge: Agentic Platform for Autonomous Data Engineering Authors:Xinyuan Wang, Hongyu Cao, Kunpeng Liu, Yanjie Fu View a PDF of the paper titled Dataforge: Agentic Platform for Autonomous Data Engineering, by Xinyuan Wang and 3 other authors View PDF HTML (experimental) Abstract:The growing demand for artificial intelligence (AI) applications in materials discovery, molecular modeling, and climate science has made data preparation a critical but labor-intensive bottleneck. Raw data from diverse sources must be cleaned, normalized, and transformed to become AI-ready, where effective feature transformation and selection are essential for robust learning. We present Dataforge, an LLM-powered agentic data engineering platform for tabular data that is automatic, safe, and non-expert friendly. It autonomously performs data cleaning and iteratively optimizes feature operations under a budgeted feedback loop with automatic stopping. Across tabular benchmarks, it achieves the best overall downstream performance; ablations further confirm the roles of routing/iterative refinement and grounding in accuracy and reliability. Dataforge demonstrates a practical path toward autonomous data agents that transform raw data from data to better data. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2511.06185 [cs.AI] (or arXiv:2511.0618...
