Artificial General Intelligence falls short when communicating role specific nuances to other systems. This is more pronounced when building autonomous LLM agents capable and designed to communicate with each other for real world problem solving. Humans can communicate context and domain specific nuances along with knowledge, and that has led to refinement of skills. In this work we propose and evaluate a novel method that leads to knowledge distillation among LLM agents leading to realtime human role play preserving unique contexts without relying on any stored data or pretraining. We also evaluate how our system performs better in simulated real world tasks compared to state of the art.

翻译：人工通用智能在向其他系统传递角色特定细节时存在不足，这在构建能够相互通信、旨在解决实际问题的大型语言模型自主智能体时尤为突出。人类能够传递上下文、领域特定细节以及知识，从而促进技能的提升。本研究提出并评估了一种新颖方法，该方法可实现大型语言模型智能体间的知识蒸馏，在无需依赖任何存储数据或预训练的情况下，实现实时人类角色扮演并保持独特上下文。我们还评估了系统在模拟真实世界任务中的表现，相较于现有最优技术，展现了更优性能。