arXiv · cs.AI · Zhichang Yu, Baining Cao, Sbaaoui Idriss, Qi Wang, Gangqiang Xu, Ling Qin, Xiaoling Jin, An Liu, Shaohao Cai, Xiaoyang Bao, Shengle Chen, Changchang Ding, Pengfei Zhu, Jingxiang Xu, Yifeng Mao, Lu Liu, Hai Jin, Yingfeng Zhang · 10 小时前模型评分53
论文提出面向智能体检索的知识编译层，导航树使查询 token 平均减少 38%
Context as Infrastructure: Knowledge Compilation for Retrieval in Agentic Systems with Harnesses
导读
论文提出在检索增强生成（RAG，Retrieval-Augmented Generation）的智能体检索流程中加入知识编译层，把文档加工成导航层级、实体图谱、时间线等结构化产物。
分类置信度
中 · 待人工复核
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “We describe RAGFlow's pipeline and harness (open source at this https URL ), which turns the loop into five disciplined stations and keeps the context entering the model the smallest sufficient set; together they form an agent-native database: a store that hands the agent compiled, governed context instead of a ranked list of passages.”
- “A navigation tree whose leaves are claim-evidence pairs--the mechanism we measure--cuts query tokens by 91% when evidence suffices and adds 1.9% when it does not, a 38% average reduction; this implies a 2% break-even sufficiency rate and a measured rate near 43%.”
- “A decision gate cuts latency from 140 s to 30 s, though substitution's token saving is bought with retrieval rounds and carries its own latency cost.”
- “under a competent harness, structural artifacts gave no detectable accuracy gain on FRAMES, and a PageIndex-style outline degraded at scale.”
来源：arXiv · cs.AI · arxiv.org