Paper Detail
Yunfei Zhong, Yinqiong Cai, Lixin Su, Haosheng Qian, Lixin Zou, Yixing Fan, Sheng Xu, Jiafeng Guo, Daiting Shi, Jingzhou He
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.
No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.
No ranking explanation is available yet.
No tags.
@article{zhong2026ranked,
title = {From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search},
author = {Yunfei Zhong and Yinqiong Cai and Lixin Su and Haosheng Qian and Lixin Zou and Yixing Fan and Sheng Xu and Jiafeng Guo and Daiting Shi and Jingzhou He},
year = {2026},
abstract = {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 docum},
url = {https://arxiv.org/abs/2609.23354},
keywords = {cs.IR},
eprint = {2609.23354},
archiveprefix = {arXiv},
}
{}