Polaris：用于对话式企业分析的多智能体系统
Polaris : Multi Agentic System for Conversational Enterprise Analytics
AI总结：
该研究提出名为Polaris的多智能体框架，通过动态任务协调（DTC）层与ReAct风格智能体结合，实现对话式企业分析，在结构化企业数据集上验证了其高语义保真度与答案相关性，为大规模可信端到端商业智能提供了新方案。
AI中文摘要：
在当今快节奏的环境中，快速访问、理解和利用数据的能力不再是可选的，而是必不可少的。然而，大多数组织仍然数据丰富但见解匮乏，受到查询、解释和说明企业规模信息的复杂性的限制。我们提出Polaris，这是一个由主管主导的用于对话式企业分析的多智能体框架，它弥合了这一差距。Polaris引入了动态任务协调（Dynamic Task Coordination，DTC），这是一个决策理论编排层，将智能体-任务分配建模为自适应二分匹配，支持跨专门用于查询、可视化和推理的智能体的实时协调、恢复和优化。通过将DTC与优先推理的ReAct风格智能体相结合，Polaris将自然语言查询转换为连贯的分析工作流，不仅检索和可视化数据，还解释潜在的“为什么”。在结构化企业数据集上的评估显示出高语义保真度和答案相关性，强调了多智能体编排在大规模提供可信端到端商业智能方面的潜力。
英文摘要：
In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential. Yet most organizations remain data-rich but insight-poor, constrained by the complexity of querying, interpreting, and explaining enterprise-scale information. We present Polaris, a supervisor-led multi-agent framework for conversational enterprise analytics that bridges this gap. Polaris introduces Dynamic Task Coordination (DTC), a decision-theoretic orchestration layer that models agent-task assignment as adaptive bipartite matching, enabling real-time coordination, recovery, and optimization across specialized agents for querying, visualization, and reasoning. By coupling DTC with reason-first, ReAct-style agents, Polaris transforms natural-language queries into coherent analytical workflows that not only retrieve and visualize data but also explain the underlying "why." Evaluation on structured enterprise datasets demonstrates high semantic fidelity and answer relevancy, underscoring the potential of multi-agent orchestration to deliver trustworthy, end-to-end business intelligence at scale.
