Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents

• URL: http://arxiv.org/abs/2511.15074v1
• Date: Wed, 19 Nov 2025 03:27:14 GMT
• Title: Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents
• Authors: Henrik Bradland, Morten Goodwin, Vladimir I. Zadorozhny, Per-Arne Andersen,
• Abstract summary: Rogue One is a novel, multi-agent framework for knowledge-informed automatic feature extraction.<n>We demonstrate that Rogue One significantly outperforms state-of-the-art methods on a comprehensive suite of 19 classification and 9 regression datasets.
• Score: 3.913122709822389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of machine learning models on tabular data is critically dependent on high-quality feature engineering. While Large Language Models (LLMs) have shown promise in automating feature extraction (AutoFE), existing methods are often limited by monolithic LLM architectures, simplistic quantitative feedback, and a failure to systematically integrate external domain knowledge. This paper introduces Rogue One, a novel, LLM-based multi-agent framework for knowledge-informed automatic feature extraction. Rogue One operationalizes a decentralized system of three specialized agents-Scientist, Extractor, and Tester-that collaborate iteratively to discover, generate, and validate predictive features. Crucially, the framework moves beyond primitive accuracy scores by introducing a rich, qualitative feedback mechanism and a "flooding-pruning" strategy, allowing it to dynamically balance feature exploration and exploitation. By actively incorporating external knowledge via an integrated retrieval-augmented (RAG) system, Rogue One generates features that are not only statistically powerful but also semantically meaningful and interpretable. We demonstrate that Rogue One significantly outperforms state-of-the-art methods on a comprehensive suite of 19 classification and 9 regression datasets. Furthermore, we show qualitatively that the system surfaces novel, testable hypotheses, such as identifying a new potential biomarker in the myocardial dataset, underscoring its utility as a tool for scientific discovery.

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