How to ensure consistent LLM agent responses?
Modeling Response Consistency in Multi-Agent LLM Systems: A Comparative Analysis of Shared and Separate Context Approaches
This paper explores how to best manage context (information) in multi-agent systems using Large Language Models (LLMs). It compares two approaches: a single LLM handling all topics with shared memory, and multiple LLMs, each with its own private memory, collaborating on related topics. The key point is that sharing memory is simpler but risks overload, while separate memories are more scalable but slower due to the need for agents to communicate. The paper introduces a metric (RCI) and mathematical model to quantify the trade-off between consistency and speed, demonstrating how factors like limited memory and noisy input affect both architectures. It concludes that balancing memory retention and noise management is crucial in LLM-based multi-agent systems.