发表机构
Walmart Global Tech（沃尔玛全球科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模零售商品定价管理难题，提出上下文感知多智能体框架自动化构建Lines and Ladders价格分类，3智能体系统在Lines任务F1达0.83，在多品类数据上表现优异且已投入生产。
AI 中文摘要
对于全球零售商而言，维持价格一致性并执行每日低价（Every Day Low Price）策略至关重要。然而，当商品目录涵盖数百万个活跃商品时，手动管理价格关系是不可行的。不同商品变体间的定价不一致会扭曲客户的价值感知并蚕食销售额。为解决这一问题，我们提出了一种可扩展的上下文感知多智能体框架，旨在自动化构建“Lines and Ladders”价格分类体系。该框架采用专门的大语言模型（LLM）智能体，通过识别关键属性、提取多模态值并应用分层分组逻辑来构建这些连贯的定价结构。在真实企业数据上进行评估并已投入生产部署，我们的3智能体系统在Lines任务上的F1分数达到0.83，通过缓解认知过载优于单智能体基线；该系统在食品与消耗品类别的准确率超过90%、召回率超过75%，在非结构化的杂货商品目录中实现了80.2%的分配准确率。
英文摘要
Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions of active items, manual governance of price relationships is infeasible. Inconsistent pricing across item variants distorts customer value perception and cannibalizes sales. To address this, we present a scalable, context-aware Multi-Agent Framework designed to automate the construction of "Lines and Ladders" pricing taxonomies. Our framework employs specialized LLM agents to construct these coherent pricing structures by identifying key attributes, extracting multi-modal values, and applying hierarchical grouping logic. Evaluated on real-world enterprise data and deployed in production, our 3-Agent system achieves an F1-score of 0.83 for Lines, outperforming single-agent baselines by mitigating cognitive overload. The system achieves >90% precision and >75% recall in Food & Consumables, and 80.2% assignment accuracy in the unstructured General Merchandise catalog.
Comments8 pages. Accepted in the Main Conference of IEEE ICMLA 2026
