AnnCoder: A Mti-Agent-Based Code Generation and Optimization Model
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| Publicado en: | Symmetry vol. 17, no. 7 (2025), p. 1087-1112 |
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| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/sym17071087 |2 doi | |
| 035 | |a 3233253290 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Zhang, Zhenhua |u College of Software, Taiyuan University of Technology, Taiyuan 030024, China; zhangzhenhua5824@link.tyut.edu.cn (Z.Z.); wyp_nanxun@163.com (Y.W.) | |
| 245 | 1 | |a AnnCoder: A Mti-Agent-Based Code Generation and Optimization Model | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The rapid progress of Large Language Models (LLMs) has greatly improved natural language tasks like code generation, boosting developer productivity. However, challenges persist. Generated code often appears “pseudo-correct”—passing functional tests but plagued by inefficiency or redundant structures. Many models rely on outdated methods like greedy selection, which trap them in local optima, limiting their ability to explore better solutions. We propose AnnCoder, a multi-agent framework that mimics the human “try-fix-adapt” cycle through closed-loop optimization. By combining the exploratory power of simulated annealing with the targeted evolution of genetic algorithms, AnnCoder balances wide-ranging searches and local refinements, dramatically increasing the likelihood of finding globally optimal solutions. We speculate that traditional approaches may struggle due to narrow optimization focuses. AnnCoder addresses this by introducing dynamic multi-criteria scoring, weighing functional correctness, efficiency (e.g., runtime/memory), and readability. Its adaptive temperature control dynamically modulates the cooling schedule, slowing cooling when solutions are diverse to encourage exploration, then accelerating convergence as they stabilize. This design elegantly avoids the pitfalls of earlier models by synergistically combining global exploration with local optimization capabilities. After conducting thorough experiments with multiple LLMs analyses across four problem-solving and program synthesis benchmarks—AnnCoder showcased remarkable code generation capabilities—HumanEval 90.85%, MBPP 90.68%, HumanEval-ET 85.37%, and EvalPlus 84.8%. AnnCoder has outstanding advantages in solving general programming problems. Moreover, our method consistently delivers superior performance across various programming languages. | |
| 653 | |a Language | ||
| 653 | |a Collaboration | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Debugging | ||
| 653 | |a Optimization | ||
| 653 | |a Functional testing | ||
| 653 | |a Closed loops | ||
| 653 | |a Temperature control | ||
| 653 | |a Automation | ||
| 653 | |a Chatbots | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Problem solving | ||
| 653 | |a Optimization models | ||
| 653 | |a Natural language | ||
| 653 | |a Cooling | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Large language models | ||
| 653 | |a Programming languages | ||
| 653 | |a Redundant components | ||
| 653 | |a Local optimization | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Algorithms | ||
| 653 | |a Simulated annealing | ||
| 653 | |a Software engineering | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Wang, Jianfeng |u College of Software, Taiyuan University of Technology, Taiyuan 030024, China; zhangzhenhua5824@link.tyut.edu.cn (Z.Z.); wyp_nanxun@163.com (Y.W.) | |
| 700 | 1 | |a Li Zhengyang |u Department of Computer Science, DigiPen Institute of Technology, Redmond, WA 98052, USA; zhengyang.li@digipen.edu | |
| 700 | 1 | |a Wang, Yunpeng |u College of Software, Taiyuan University of Technology, Taiyuan 030024, China; zhangzhenhua5824@link.tyut.edu.cn (Z.Z.); wyp_nanxun@163.com (Y.W.) | |
| 700 | 1 | |a Zheng Jiayun |u College of Engineering, University of Michigan Ann Arbor, Ann Arbor, MI 48104, USA | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 7 (2025), p. 1087-1112 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233253290/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233253290/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233253290/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |