Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction

• URL: http://arxiv.org/abs/2605.25297v2
• Date: Wed, 27 May 2026 18:29:52 GMT
• Title: Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
• Abstract summary: We present Eureka, an agentic code generation framework for predictive model performance.<n>Eureka has three stages: Expert Agent, Feature Factory, and Self-Evolving Alignment Engine.<n>Eureka consistently outperforms AutoFE and LLM-based baselines on 7 public benchmarks in healthcare, finance, and social domains.
• Score: 2.101720002743541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applications. We define feature engineering as an agentic code generation problem: features are not static data transformations, but executable programs that can be generated, evaluated, and iteratively improved. We present Eureka, an LLM-driven framework with three stages. (1) An Expert Agent, fine-tuned via SFT on domain knowledge, produces structured feature design plans in JSON format. (2) An LLM Feature Factory translates each plan into executable Python code through chain-of-thought reasoning, turning feature hypotheses into runnable programs. (3) A Self-Evolving Alignment Engine uses Reinforcement Learning (GRPO) with dual-channel reward (metric-based utility + semantic alignment) to enhance code quality. By expressing features as programs, the learned generation patterns can transfer across domains. Evaluated on 7 public benchmarks in healthcare, finance, and social domains, Eureka consistently outperforms both traditional AutoFE and LLM-based baselines. We further demonstrate Eureka's effectiveness on cloud GPU resource demand prediction at Alibaba Cloud, where Eureka improves demand fulfillment rate by 16% and lowers computing resource migration rates by 33%.

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