FutureHouse 详解 PaperQA2：如何让科学 RAG 超越科学家表现
FutureHouse 发布工程博客，介绍其科学 RAG 系统 PaperQA2 的设计实验，称其在回答高难度科学问题、撰写综述文章和检测文献矛盾等任务上超过科学家的表现。团队基于 LitQA2 的 200 道专家多选题评测发现，智能体式查询扩展、LLM 重排与上下文摘要（RCS）、引用遍历对准确率重要，而嵌入模型选择、混合关键词嵌入、chunk 大小和结构化解析影响不大。新增的引用遍历工具通过 Semantic Scholar 和 Crossref API 前后各遍历一度引用，提升了正确论文的召回率。
to score the relevance of the chunk (between 1 and 10) along with providing its summary ( 95% recall at a depth of 20. We also appended a keyword similarity embedding to our large OpenAI embeddings, called “Hybrid” embeddings below. A simple keyword vector was used with a dimension of 256, normalized to a value of 1. OpenAI’s tiktoken library encoded each chunk into a count-encoded vector which counted each token as the token ID modulo 256. This method showed a small decrease in the mean rank, m
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