BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution
The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal. For instance, on LiveCodeBench, frontier models achieve over 99% Pass@1 on easy splits and exceed 90% Pass@1 on average across difficulty levels. Constructing new, challenging datasets typically requires substantial human effort, creating a bottleneck for progress. We introduce BenchEvolver, a solution-centric evolutionary framework that automatically transforms existing coding problems into harder variants. Rather than generating problems from scratch, BenchEvolver evolves referenc...
| Authors | Yangzhen Wu, Aaron J. Li, Wenjie Ma et al. |
|---|---|
| Primary category | cs.SE |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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