How Generation Architecture Shapes Code Complexity in Multi-Agent LLM Systems: A Paired Study on HumanEval
arXiv:2606.00308v1 Announce Type: cross Abstract: Large-language-model code generation has shifted from single-shot prompting to multi-agent orchestrations - analyst, coder, tester, and debugger pipelines - and is evaluated almost exclusively on functional correctness. Whether these architectures also affect the structural complexity of the code they produce, and which orchestration layers carry the cost, remains largely unexamined: prior work has documented prompt-level effects on code complexity, but the architecture-level question is open. We compare six widely-used multi-agent configuratio
The rapid advancement of large language models and their application in multi-agent systems for code generation necessitates a deeper understanding of their implications beyond just functional correctness. This research addresses a gap in understanding how architectural choices impact code quality and efficiency.
This research provides critical insights into optimizing multi-agent LLM systems for code generation, moving beyond mere functional correctness to include factors like code complexity, which impacts maintainability, scalability, and security of AI-generated software. This directly influences developer productivity and software engineering practices for AI-driven development.
The understanding of how different multi-agent architectures influence the structural complexity of generated code will inform better design and implementation of AI agents for software development. This moves towards a more holistic evaluation criteria for LLM code generation beyond just functional outputs.
- · AI software developers
- · Companies adopting multi-agent LLM systems
- · AI research in code generation
- · Inefficient multi-agent LLM architectures
- · Traditional software development methods (long-term)
Improved efficiency and quality of AI-generated code through optimized multi-agent system designs.
Reduced technical debt in AI-generated software, accelerating product development cycles and reducing maintenance costs.
A shift in software engineering education and practice to include AI agent orchestration and evaluation for code quality metrics beyond test suites.
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Read at arXiv cs.LG