In the rapidly evolving field of artificial intelligence, the ability to harness and integrate knowledge across various domains stands as a paramount challenge and opportunity. This study introduces a novel approach to cross-domain knowledge discovery through the deployment of multi-AI agents, each specialized in distinct knowledge domains. These AI agents, designed to function as domain-specific experts, collaborate in a unified framework to synthesize and provide comprehensive insights that transcend the limitations of single-domain expertise. By facilitating seamless interaction among these agents, our platform aims to leverage the unique strengths and perspectives of each, thereby enhancing the process of knowledge discovery and decision-making. We present a comparative analysis of the different multi-agent workflow scenarios evaluating their performance in terms of efficiency, accuracy, and the breadth of knowledge integration. Through a series of experiments involving complex, interdisciplinary queries, our findings demonstrate the superior capability of domain specific multi-AI agent system in identifying and bridging knowledge gaps. This research not only underscores the significance of collaborative AI in driving innovation but also sets the stage for future advancements in AI-driven, cross-disciplinary research and application. Our methods were evaluated on a small pilot data and it showed a trend we expected, if we increase the amount of data we custom train the agents, the trend is expected to be more smooth.

翻译：在人工智能快速发展的领域中，跨领域知识的整合与利用既是重大挑战也是关键机遇。本研究提出了一种通过部署多AI智能体实现跨领域知识发现的新方法，每个智能体专精于不同知识领域。这些被设计为领域专家的AI智能体在统一框架中协作，综合提供超越单一领域专业局限的全面洞察。通过促进各智能体间的无缝交互，我们的平台旨在发挥各自独特优势与视角，从而增强知识发现与决策过程。我们对比分析了不同多智能体工作流场景在效率、准确性和知识整合广度方面的表现。通过一系列涉及复杂跨学科查询的实验，研究结果表明，领域专用的多AI智能体系统在识别和弥合知识鸿沟方面具有显著优势。本研究不仅强调了协作式AI在推动创新中的重要性，更为未来以AI驱动的跨学科研究与应用发展奠定了基础。我们的方法在小型试点数据上进行了评估，结果显示出预期趋势：随着用于定制训练智能体的数据量增加，该趋势预计将更为平滑。