探测-然后规划：面向工业电商搜索的环境感知规划
Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search
AI总结：
本文提出环境感知搜索规划(EASP)，通过探测-规划机制解决电商搜索中盲点与延迟的矛盾，通过离线数据合成、规划器训练和在线服务适应性路由提升搜索相关召回率和UCVR、GMV。
摘要：
现代电商搜索正在演变以解决复杂的用户意图。尽管大语言模型(LLMs)具有强大的推理能力，但现有基于LLM的范式面临一个根本性的盲点-延迟矛盾：查询重写对检索能力及实时库存无感知，导致无效计划；反之，深度搜索代理依赖迭代工具调用和反思，造成数秒延迟，无法满足工业次秒预算。为解决这一冲突，我们提出环境感知搜索规划(EASP)，将搜索规划重新表述为一种基于现实环境的动态推理过程。EASP引入探测-然后规划机制：轻量级检索探测暴露检索快照，使规划器诊断执行缺口并生成基于现实的搜索计划。该方法包含三个阶段：(1) 离线数据合成：教师代理通过诊断探测环境合成多样、执行验证的计划。(2) 规划器训练和对齐：规划器通过监督微调(SFT)内化诊断能力，再通过强化学习(RL)与业务结果(转化率)对齐。(3) 自适应在线服务：复杂度感知路由机制选择性激活复杂查询的规划，确保最优资源分配。广泛的离线评估和京东(JD.com)的在线A/B测试表明，EASP显著提高了相关召回率，并在UCVR和GMV上实现了显著提升。EASP已成功部署在京东的AI搜索系统中。
英文摘要：
Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based paradigms face a fundamental blindness-latency dilemma: query rewriting is agnostic to retrieval capabilities and real-time inventory, yielding invalid plans; conversely, deep search agents rely on iterative tool calls and reflection, incurring seconds of latency incompatible with industrial sub-second budgets. To resolve this conflict, we propose Environment-Aware Search Planning (EASP), reformulating search planning as a dynamic reasoning process grounded in environmental reality. EASP introduces a Probe-then-Plan mechanism: a lightweight Retrieval Probe exposes the retrieval snapshot, enabling the Planner to diagnose execution gaps and generate grounded search plans. The methodology comprises three stages: (1) Offline Data Synthesis: A Teacher Agent synthesizes diverse, execution-validated plans by diagnosing the probed environment. (2) Planner Training and Alignment: The Planner is initialized via Supervised Fine-Tuning (SFT) to internalize diagnostic capabilities, then aligned with business outcomes (conversion rate) via Reinforcement Learning (RL). (3) Adaptive Online Serving: A complexity-aware routing mechanism selectively activates planning for complex queries, ensuring optimal resource allocation. Extensive offline evaluations and online A/B testing on JD.com demonstrate that EASP significantly improves relevant recall and achieves substantial lifts in UCVR and GMV. EASP has been successfully deployed in JD.com's AI-Search system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
