优化提示词提升多智能体协作效率，支持语音交互。
Improving Cooperation in Collaborative Embodied AI
- 通过提示词工程优化智能体协作策略
- 使用Gemma3时系统效率提升22%
- 新增语音交互，便于开发与演示
将大型语言模型（LLMs）引入多智能体系统，为协同推理与合作提供了新可能。本文研究不同提示方法，评估其在提升智能体协作行为与决策能力方面的效果。我们改进了CoELA框架，该框架旨在构建利用LLM进行多智能体通信、推理和任务协调的协作式具身智能体。通过系统性实验，考察不同LLM与提示工程策略的组合，以识别最优配置，最大化协作性能。此外，我们还集成语音功能，实现无缝的基于语音的协同交互。结果表明，提示优化能显著提升协作表现：最佳组合使基于Gemma3的系统效率相比原始CoELA系统提升22%。同时，语音集成提升了系统的交互体验，有利于迭代开发与演示。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) into multiagent systems has opened new possibilities for collaborative reasoning and cooperation with AI agents. This paper explores different prompting methods and evaluates their effectiveness in enhancing agent collaborative behaviour and decision-making. We enhance CoELA, a framework designed for building Collaborative Embodied Agents that leverage LLMs for multi-agent communication, reasoning, and task coordination in shared virtual spaces. Through systematic experimentation, we examine different LLMs and prompt engineering strategies to identify optimised combinations that maximise collaboration performance. Furthermore, we extend our research by integrating speech capabilities, enabling seamless collaborative voice-based interactions. Our findings highlight the effectiveness of prompt optimisation in enhancing collaborative agent performance; for example, our best combination improved the efficiency of the system running with Gemma3 by 22% compared to the original CoELA system. In addition, the speech integration provides a more engaging user interface for iterative system development and demonstrations.
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