@inproceedings{hu-etal-2026-controlled,
title = "Controlled Self-Evolution for Algorithmic Code Optimization",
author = "Hu, Tu and
Chen, Ronghao and
Zhang, Shuo and
Xu, Rui and
Yin, Jianghao and
Feng, Mou Xiao and
Liu, Jingping and
Jiang, Wenqi and
Zhang, Shaolei and
Fang, Yuqi and
Hu, Sen and
Xu, Yi and
Wang, Huacan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1036/",
pages = "21886--21916",
ISBN = "979-8-89176-462-0",
abstract = "Self-evolution methods enhance code generation through iterative ``generate-verify-refine'' cycles, yet existing approaches suffer from low exploration efficiency, failing to discover algorithmically superior solutions within limited budgets. We identify the root cause as the lack of systematic controllability: execution feedback is not reliably converted into targeted search decisions across route planning, edit granularity, and experience reuse. We propose \textbf{C}ontrolled \textbf{S}elf-\textbf{E}volution (CSE), a framework that couples three components into an integrated control architecture. Diversified Planning Initialization provides route diversity through structurally distinct algorithmic strategies. Modular Evolution provides feedback attribution and module-level editing by decomposing programs into functional modules and applying targeted mutation and compositional crossover. Hierarchical Evolution Memory maintains search state within tasks and distills reusable optimization patterns across tasks. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Further analysis reveals non-additive synergy among control layers, confirming that CSE{'}s effectiveness arises from component coupling rather than independent contributions. The code and evaluation implementation are publicly available at \url{https://github.com/QuantaAlpha/EvoControl}."
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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 algorithmically superior solutions within limited budgets. We identify the root cause as the lack of systematic controllability: execution feedback is not reliably converted into targeted search decisions across route planning, edit granularity, and experience reuse. We propose Controlled Self-Evolution (CSE), a framework that couples three components into an integrated control architecture. Diversified Planning Initialization provides route diversity through structurally distinct algorithmic strategies. Modular Evolution provides feedback attribution and module-level editing by decomposing programs into functional modules and applying targeted mutation and compositional crossover. Hierarchical Evolution Memory maintains search state within tasks and distills reusable optimization patterns across tasks. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Further analysis reveals non-additive synergy among control layers, confirming that CSE’s effectiveness arises from component coupling rather than independent contributions. The code and evaluation implementation are publicly available at https://github.com/QuantaAlpha/EvoControl.</abstract>
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%0 Conference Proceedings
%T Controlled Self-Evolution for Algorithmic Code Optimization
%A Hu, Tu
%A Chen, Ronghao
%A Zhang, Shuo
%A Xu, Rui
%A Yin, Jianghao
%A Feng, Mou Xiao
%A Liu, Jingping
%A Jiang, Wenqi
%A Zhang, Shaolei
%A Fang, Yuqi
%A Hu, Sen
%A Xu, Yi
%A Wang, Huacan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hu-etal-2026-controlled
%X Self-evolution methods enhance code generation through iterative “generate-verify-refine” cycles, yet existing approaches suffer from low exploration efficiency, failing to discover algorithmically superior solutions within limited budgets. We identify the root cause as the lack of systematic controllability: execution feedback is not reliably converted into targeted search decisions across route planning, edit granularity, and experience reuse. We propose Controlled Self-Evolution (CSE), a framework that couples three components into an integrated control architecture. Diversified Planning Initialization provides route diversity through structurally distinct algorithmic strategies. Modular Evolution provides feedback attribution and module-level editing by decomposing programs into functional modules and applying targeted mutation and compositional crossover. Hierarchical Evolution Memory maintains search state within tasks and distills reusable optimization patterns across tasks. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Further analysis reveals non-additive synergy among control layers, confirming that CSE’s effectiveness arises from component coupling rather than independent contributions. The code and evaluation implementation are publicly available at https://github.com/QuantaAlpha/EvoControl.
%U https://aclanthology.org/2026.findings-emnlp.1036/
%P 21886-21916
Markdown (Informal)
[Controlled Self-Evolution for Algorithmic Code Optimization](https://aclanthology.org/2026.findings-emnlp.1036/) (Hu et al., Findings 2026)
ACL
- Tu Hu, Ronghao Chen, Shuo Zhang, Rui Xu, Jianghao Yin, Mou Xiao Feng, Jingping Liu, Wenqi Jiang, Shaolei Zhang, Yuqi Fang, Sen Hu, Yi Xu, and Huacan Wang. 2026. Controlled Self-Evolution for Algorithmic Code Optimization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21886–21916, Budapest, Hungary. Association for Computational Linguistics.