用智能代理架构提升价值流模拟中的洞察提取效率与准确性。
Agentic Insight Generation in VSM Simulations
- 分步式代理结构分离调度与分析，支持多跳推理。
- 顶级模型准确率达86%，跨测试稳定表现。
- 适合需要精准决策支持的制造业流程优化场景。
从复杂的价值流图（VSM）仿真中提取可操作洞察既具挑战性又耗时易错。近期大型语言模型的发展为该任务提供了新途径。然而，现有方法虽擅长处理原始数据获取信息，却因结构限制难以捕捉此领域中相似数据源间的细微情境差异。为此，我们提出一种解耦的两阶段智能体架构：通过将编排与数据分析分离，系统融合领域专家知识实现渐进式数据发现。该架构使编排模块能智能选择数据源，并在数据结构间执行多跳推理，同时保持轻量级内部上下文。多个最先进大模型的实验结果表明该框架可行：顶尖模型准确率最高达86%，且在多次评估中表现出高鲁棒性。
原文摘要 · Abstract (English)
Extracting actionable insights from complex value stream map simulations can be challenging, time-consuming, and error-prone. Recent advances in large language models offer new avenues to support users with this task. While existing approaches excel at processing raw data to gain information, they are structurally unfit to pick up on subtle situational differences needed to distinguish similar data sources in this domain. To address this issue, we propose a decoupled, two-step agentic architecture. By separating orchestration from data analysis, the system leverages progressive data discovery infused with domain expert knowledge. This architecture allows the orchestration to intelligently select data sources and perform multi-hop reasoning across data structures while maintaining a slim internal context. Results from multiple state-of-the-art large language models demonstrate the framework's viability: with top-tier models achieving accuracies of up to 86% and demonstrating high robustness across evaluation runs.
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