用于基于知识的预测的可追踪多智能体系统
Traceable Multi-Agent System for Knowledge-Based Forecasting
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中文总结 AI 辅助
本文提出可追踪多智能体预测演示系统TraceMAS，通过两类因果环图关联证据与预测，在原油价格预测中验证其可让自主智能体兼顾灵活性与过程可检查性。
中文摘要 AI 辅助
企业预测越来越依赖于自主智能体，这些智能体可解读文档、搜索数据、生成代码并修订模型。这种自主性虽有助于构建自适应预测流程，但也让从业者难以检查预测为何改变、哪些证据支撑了该改变，以及数据和建模选择如何修订。我们提出TraceMAS，这是一款用于可追踪多智能体预测的交互式演示系统。TraceMAS围绕两种因果环表示来组织智能体输出：理想因果环图（Ideal CLD），捕获从领域文档中提取的关键因素及其因果关系；数据驱动因果环图（Data-Grounded CLD），将这些因素链接到内部变量、外部数据或记录的代理变量。Data-Grounded CLD指导特征构建和模型设计，同时保留文本证据、数据选择与模型修订之间的关联。我们在原油价格预测上演示了TraceMAS。该演示界面允许用户比较预测迭代、检查智能体级修订、探索因果图、查看特征-数据映射和模型架构，并将情景预测与市场叙事关联起来。此演示表明，自主预测智能体可在保持灵活性的同时，使从证据到预测的过程具备可检查性。
英文摘要
Enterprise forecasting increasingly relies on autonomous agents that interpret documents, search for data, generate code, and revise models. While this autonomy helps build adaptive forecasting pipelines, it also makes it difficult for practitioners to inspect why a forecast changed, which evidence supported the change, and how data and modeling choices were revised. We present TraceMAS, an interactive demo system for traceable multi-agent forecasting. TraceMAS organizes agent outputs around two causal-loop representations: an Ideal Causal Loop Diagram (Ideal CLD), which captures key factors and their causal relations extracted from domain documents, and a Data-Grounded Causal Loop Diagram (Data-Grounded CLD), which links those factors to internal variables, external data, or documented proxies. The Data-Grounded CLD guides feature construction and model design while preserving the connection between textual evidence, data choices, and model revisions. We demonstrate TraceMAS on crude oil price forecasting. The demo interface allows users to compare forecasting iterations, inspect agent-level revisions, explore causal maps, review feature-data mappings and model architecture, and connect scenario forecasts to market narratives. This demonstration shows how autonomous forecasting agents can retain flexibility while making the evidence-to-forecast process inspectable.
