Apr 23
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
★★★★★
significance 2/5
The paper introduces MALMAS, a multi-agent system that uses a memory-augmented LLM to automate feature generation for tabular data. It utilizes a router agent and various memory modules to improve the diversity and quality of features compared to traditional methods.
Why it matters
Automating complex feature engineering through multi-agent memory architectures signals a shift toward autonomous, high-level data science workflows.
Tags
#llm #multi-agent #feature engineering #tabular data #automated machine learningRelated coverage
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