CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
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.
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Zi-Han Wang
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2026-03-16T10:40:58.885Z
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Lam Nguyen
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2026-03-16T11:12:41.978Z
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Lam Nguyen
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Zhengyang Zhao
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2026-03-16T11:12:48.479Z
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Zhengyang Zhao
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Mengyue Yang
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2026-03-16T11:12:58.518Z
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Mengyue Yang
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Chengwei Qin
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Yujiu Yang
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2026-03-16T11:13:13.778Z
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Yujiu Yang
Thu-redrobot
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Thu-redrobot
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Linyi Yang
CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
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.
Researchers developed CreativeBench, a benchmark for evaluating machine creativity in code generation, and proposed EvoRePE, a method to enhance creative output through evolutionary search patterns.
AlphaEvolve CreativeBench CreativeBench-Combo CreativeBench-Explore reverse engineering self-play executable code quality novelty scaling convergence-by-scaling reasoning capabilities constrained exploration combination EvoRePE evolutionary search patterns