dorsal/arxiv
View SchemaControlled Self-Evolution for Algorithmic Code Optimization
| Authors | Tu Hu, Ronghao Chen, Shuo Zhang, Jianghao Yin, Mou Xiao Feng, Jingping Liu, Shaolei Zhang, Wenqi Jiang, Yuqi Fang, Sen Hu, Huacan Wang, Yi Xu |
|---|---|
| Categories | |
| ArXiv ID | 2601.07348vv4 |
| URL | https://arxiv.org/abs/2601.07348 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback-guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter-task and intra-task levels. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.
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"date_created": "2026-02-17T05:53:12.389000Z",
"date_modified": "2026-02-17T05:53:12.389000Z",
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"abstract": "Self-evolution methods enhance code generation through iterative \"generate-verify-refine\" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback-guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter-task and intra-task levels. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.",
"arxiv_id": "2601.07348",
"authors": [
"Tu Hu",
"Ronghao Chen",
"Shuo Zhang",
"Jianghao Yin",
"Mou Xiao Feng",
"Jingping Liu",
"Shaolei Zhang",
"Wenqi Jiang",
"Yuqi Fang",
"Sen Hu",
"Huacan Wang",
"Yi Xu"
],
"categories": [
"cs.CL",
"cs.AI",
"cs.NE"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Controlled Self-Evolution for Algorithmic Code Optimization",
"url": "https://arxiv.org/abs/2601.07348",
"version": "v4"
},
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"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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