<ul><li>Feature engineering is crucial for enhancing AI model performance by transforming raw data into discriminative features.</li><li>A new concept called agentic feature augmentation is introduced to unify feature selection and generation through a Multi-Agent System with Long and Short-Term Memory (MAGS).</li><li>MAGS includes a selector agent to eliminate redundant features, a generator agent to create informative new dimensions, and a router agent for coordination.</li><li>The framework utilizes in-context learning, short-term memory for immediate feedback, long-term memory for global guidance, and a reinforcement fine-tuning technique for effective decision-making in navigating feature space.</li></ul>

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

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