How Generation Architecture Shapes Code Complexity in Multi-Agent LLM Systems: A Paired Study on HumanEval
Summary
A study investigated how multi-agent LLM code generation architectures influence the structural complexity of generated code, moving beyond typical functional correctness evaluations. Researchers compared six widely-used multi-agent configurations (Basic, AC, ACT, Debugger, AC+Debugger, ACT+Debugger) using two models from the GPT-4o family across all 164 HumanEval tasks, generating 1,968 paired observations. Code complexity was measured with five RADON metrics: SLOC, cyclomatic complexity, and Halstead Volume, Difficulty, and Effort. The findings reveal that the six architectures collapse into two distinct complexity clusters, separated by a 50-130% gap, consistent across models and conditions. The analyst-coder split significantly inflates complexity, while the runtime debugger actively deflates it, and the tester re-inflates it. Crucially, the heavier, more complex architectures showed no pass@1 advantage over leaner ones.
Key takeaway
For AI Scientists or Directors of AI/ML designing multi-agent LLM code generation systems, you should critically evaluate architectural choices beyond just functional correctness. Your team's architectural elaborations, like analyst-coder splits, can significantly inflate code complexity by 50-130% without improving pass@1 accuracy. Prioritize leaner architectures. Consider integrating runtime debuggers to actively deflate complexity. Ensure any added architectural layers are justified by measured benefits on critical dimensions like maintainability or cost.
Key insights
Multi-agent LLM code generation architectures significantly impact code complexity without necessarily improving functional correctness.
Principles
- Architectural elaboration inflates code complexity.
- Increased complexity does not guarantee better pass@1 scores.
- Analyst-coder split increases complexity; debuggers reduce it.
Method
A paired non-parametric statistical pipeline (Friedman omnibus, Wilcoxon signed-rank post-hoc with Holm correction, Kendall's $W$ and matched-pairs rank-biserial effect sizes) was applied in all-completions and passing-only conditions.
In practice
- Evaluate architectural benefits beyond functional correctness.
- Prioritize lean architectures for LLM code generation.
- Consider debugger layers to reduce code complexity.
Topics
- Multi-Agent Systems
- LLM Code Generation
- Code Complexity
- HumanEval
- GPT-4o
- Software Architecture
Best for: AI Architect, AI Engineer, Machine Learning Engineer, AI Scientist, Research Scientist, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.