The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve. However, the progress of such systems is hindered by the lack of rigorous, quantitative evaluation. To tackle this challenge, we introduce CreativeBench, a benchmark for evaluating machine creativity in code generation, grounded in a classical cognitive framework. Comprising two subsets -- CreativeBench-Combo and CreativeBench-Explore -- the benchmark targets combinatorial and exploratory creativity through an automated pipeline utilizing reverse engineering and self-play. By leveraging executable code, CreativeBench objectively distinguishes creativity from hallucination via a unified metric defined as the product of quality and novelty. Our analysis of state-of-the-art models reveals distinct behaviors: (1) scaling significantly improves combinatorial creativity but yields diminishing returns for exploration; (2) larger models exhibit ``convergence-by-scaling,'' becoming more correct but less divergent; and (3) reasoning capabilities primarily benefit constrained exploration rather than combination. Finally, we propose EvoRePE, a plug-and-play inference-time steering strategy that internalizes evolutionary search patterns to consistently enhance machine creativity.

翻译：高质量预训练数据的饱和已将研究焦点转向能够持续生成新产物的进化系统，从而推动了AlphaEvolve的成功。然而，此类系统的进展缺乏严谨的量化评估。为应对这一挑战，我们提出CreativeBench——基于经典认知框架、用于评估代码生成中机器创造力的基准测试。该基准包含两个子集：CreativeBench-Combo和CreativeBench-Explore，分别针对组合性创造力与探索性创造力，并通过逆向工程与自我博弈的自动化流程实现。借助可执行代码，CreativeBench利用统一指标（定义为质量与新颖性的乘积）客观区分创造力与幻觉。针对当前最优模型的分析揭示了三种独特行为：(1) 规模化显著提升组合性创造力，但对探索性创造力收益递减；(2) 更大模型呈现“规模化收敛”现象，即正确性提高但发散性下降；(3) 推理能力主要助力受限探索而非组合任务。最后，我们提出EvoRePE——一种即插即用的推理时引导策略，通过内化进化搜索模式持续增强机器创造力。