从排序文档到可靠上下文：面向答案的AI搜索上下文构建框架
From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search
AI总结：
针对AI搜索中检索目标从排序文档转向构建可靠上下文的需求，提出面向答案的三阶段上下文构建框架，并在工业工作流中验证了其在检索和答案层面的有效性。
AI中文摘要：
传统Web搜索遵循面向人的范式，用户自行查看排序文档并综合信息。在AI搜索中，检索到的文档转而作为生成模型的输入，将检索目标从按搜索满意度对文档排序转变为构建可靠上下文以生成正确答案。我们将这一转变形式化为面向答案的上下文构建，通过三阶段框架实现：（1）答案支持（Answer Support）识别对答案生成有贡献信息的候选文档；（2）内容可信度（Content Trustworthiness）从来源、时间和事实角度评估该信息是否为正确答案提供可靠基础；（3）上下文组织（Context Organization）在有限上下文预算下选择、整合并结构化保留信息，以实现一致且稳健的生成。我们进一步开发了涵盖先验和后验优化的工业工作流，并建立了系统的评估协议，涵盖检索侧上下文和最终答案。实验表明，在检索层和答案层均取得一致改进，验证了该框架及其工业实现的有效性。
英文摘要：
Traditional Web search follows a human-facing paradigm in which users inspect ranked documents and synthesize information themselves. In AI Search, retrieved documents instead serve as inputs to a generation model, shifting the retrieval objective from ranking documents by Search Satisfaction to constructing reliable context for correct answer generation. We formulate this shift as answer-oriented context construction through a three-stage framework: (1) Answer Support identifies candidate documents that contribute information to answer generation; (2) Content Trustworthiness assesses whether this information provides a reliable basis for correct answers from source, temporal, and factual perspectives; and (3) Context Organization selects, consolidates, and structures retained information under a finite context budget for consistent and robust generation. We further develop an industrial workflow spanning prior and posterior optimization and establish a systematic evaluation protocol covering both retrieval-side context and final answers. Experiments show consistent improvements at both Retrieval and Answer levels, demonstrating the effectiveness of the framework and its industrial implementation.
