让智能助手与数据看板实时同步，提升分析准确性与交互体验。
TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
- 构建可执行的数字孪生看板，统一语言查询与操作状态
- 准确率提升至63.3%，超时率从40%降至10%
- 适合需要高可靠性分析的业务决策者和数据分析师
商业智能（BI）正将自然语言查询与看板操作结合，但多步骤分析中二者常不同步。用户在直接操作看板与输入自然语言之间切换时，难以保持过滤器、层级、指标和图表上下文的一致性。我们提出TwinBI，一种基于代理的数字孪生框架，将基于LLM的智能体系统与可执行的看板状态耦合。TwinBI通过统一交互日志重建共享分析状态，整合对话、操作、语义对齐与溯源追踪，并提供模式视图、SQL、日志及/insights命令等输出。在控制A/B测试中，使用相同基线智能体，TwinBI将精确匹配准确率从43.3%提升至63.3%，部分得分准确率从48.3%升至70.8%，超时率由40.0%降至10.0%。可用性研究显示，用户在集成看板与聊天的工作流中表现优异，任务准确率高，工作负载适中，对状态感知机制评价良好。结果表明，TwinBI通过将可视化状态转化为可行动上下文，同时提升了智能体可靠性与用户支持能力。数据集与源码已公开：https://github.com/simonjisu/TwinBI
原文摘要 · Abstract (English)
Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis. As users switch between direct dashboard manipulation and natural-language queries, it becomes difficult to preserve a consistent analytical state across filters, hierarchies, metrics, and chart context. We present TwinBI, an agentic digital-twin framework that couples an LLM-based agent system with an executable BI dashboard state. TwinBI unifies conversational interaction, dashboard manipulation, semantic grounding, and provenance tracking through a shared analytical state reconstructed from a unified interaction log. It also exposes artifacts such as schema views, SQL, logs, and an /insights command for state-grounded analytical summaries. We evaluate TwinBI in two complementary ways. In a controlled A/B benchmark with the same backbone agent, TwinBI improves exact-match accuracy from 43.3% to 63.3%, partial-credit accuracy from 48.3% to 70.8%, and substantially reduces timeout rate from 40.0% to 10.0% relative to Dashboard alone. In a usability study, participants benefited from the integrated dashboard-and-chat workflow, with high task accuracy, moderate workload, and favorable ratings for state-aware interaction mechanisms. These results suggest that TwinBI improves both agent-level analytical reliability and user-facing analytical support by turning visible dashboard state into richer actionable context. Our dataset and source code are available at: https://github.com/simonjisu/TwinBI
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。