ReasonCast：基于选择性语义推理的智能体需求预测
ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning
- Taobao and Tmall Group, Alibaba Group（阿里巴巴集团淘宝天猫集团）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ReasonCast是结构化语义干预框架，通过选择性语义推理结合多类信息，在三类场景降低WMAPE，且可避免稳定期无差别干预的负面影响。
AI中文摘要：
需求预测日益需要结合两类互补信息：历史销售数据揭示重复出现的数值动态，而未来促销、节假日、价格变动及平台干预提供前瞻性知识。现有文本增强预测方法常将此类上下文编码为通用表示，并与时间序列特征统一融合，未明确区分哪些语义效应对预测相关，或应如何修改未来动态。我们提出ReasonCast，一种结构化语义干预框架，将事件知识转化为预测特定操作。智能体检查事件上下文、无文本预测及其不确定性，以确定是否需要文本推理。ReasonCast不注入自由形式文本，而是通过描述事件相关性、需求方向、时间形态、幅度及峰值强度的结构化字段表示事件知识，这些字段与时间序列基础模型的时间分量选择性交互：加法路径修正局部趋势和时间形态，乘法路径捕捉事件驱动的水平偏移。ReasonCast引入基于预测的后训练课程：Schema SFT建立语义字段；语义字段RL校准方向、形态、幅度及峰值判断；预测效用RL通过冻结预测器评估语义干预，使推理输出与边际预测改进对齐。ReasonCast在节假日敏感类别、大促敏感类别及M5事件窗口上分别降低WMAPE 3.29、1.25及0.47个百分点；在销售稳定期，无差别语义干预使WMAPE升高1.68个百分点，而抑制不必要干预可保留数值主干。
英文摘要：
Demand forecasting increasingly requires combining two complementary sources of information: historical sales reveal recurring numerical dynamics, while future promotions, holidays, price changes, and platform interventions provide forward-looking knowledge. Existing text-enhanced forecasting methods often encode such context into generic representations and fuse it uniformly with time-series features, without explicitly distinguishing which semantic effects are forecast-relevant or how they should modify future dynamics. We introduce ReasonCast, a structured semantic intervention framework that translates event knowledge into forecast-specific operations. An agent examines the event context, the no-text forecast, and its uncertainty to determine whether textual reasoning is needed. Rather than injecting free-form text, ReasonCast represents event knowledge through structured fields describing event relevance, demand direction, temporal shape, amplitude, and peak intensity. These fields interact selectively with temporal components of a time-series foundation model. An additive path corrects local trends and temporal shapes, while a multiplicative path captures event-driven level shifts. ReasonCast introduces a forecast-grounded post-training curriculum. Schema SFT establishes semantic fields; semantic-field RL calibrates direction, shape, amplitude, and peak judgments; and forecast-utility RL evaluates semantic interventions through a frozen forecaster, aligning reasoning outputs with marginal forecast improvement. ReasonCast lowers WMAPE by 3.29, 1.25, and 0.47 percentage points on holiday-sensitive categories, mega-sale-sensitive categories, and M5 event windows, respectively. On stable-sales periods, indiscriminate semantic intervention increases WMAPE by 1.68 percentage points, whereas suppressing unnecessary intervention preserves the numerical backbone.
