How Generation Architecture Shapes Code Complexity in Multi-Agent LLM Systems: A Paired Study on HumanEval

1. 1Analyze the impact of multi-agent orchestrations on code complexity using HumanEval
2. 2Evaluate the effects of different orchestration layers on code structure
3. 3Apply the findings to optimize LLM system architecture for reduced code complexity
4. 4Test the optimized architecture using functional correctness metrics
5. 5Configure the LLM system to prioritize code simplicity and readability

AI engineers and researchers benefit from understanding the relationship between generation architecture and code complexity to optimize their LLM systems, while software engineers can apply these insights to improve code quality

💡 The choice of generation architecture can significantly impact the structural complexity of the code produced by LLM systems

🤖 Generation Architecture affects code complexity in multi-agent LLM systems! 📊