AskChem：以声明为中心的化学文献综合基础设施

摘要

化学文献综述通常需要汇集分散在众多出版物中的具体发现，然而现有的文献检索系统主要返回排序后的文档列表。因此，科学家和AI智能体需要手动定位相关信息、验证其来源，并跨论文组装答案。我们提出AskChem，一种以主张为中心、支持跨论文化学检索的基础设施。AskChem将检索单位从论文转变为携带溯源信息的主张：每篇论文被转化为原子化、类型化的主张，每个主张都以来源DOI和逐字引用或明确的证据定位器为支撑。在这一共享主张存储之上，AskChem提供了互补的检索与综合结构：用于分层检索和浏览的稳定分面分类法、通过关系连接主张的证据图，以及将索引论文置于科学原理之下的探索性动态分类法。AskChem目前索引了来自147K篇论文的240万条主张，并提供网页界面，以及面向AI智能体的REST、SDK和MCP访问接口。在AskChem-Bench上，将GPT-5.5阅读器锚定于AskChem可达到100%的可解析DOI，而未使用检索时为88.3%，并且在五个测试系统中拥有最高的引用密度。AskChem已上线，访问地址为https://askchem.org。

English

Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists. As a result, scientists and AI agents need to locate relevant information, verify their provenance, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure for cross-paper chemistry search. AskChem changes the unit of retrieval from the paper to the provenance-carrying claim: each paper is converted into atomic, typed claims, each grounded by a source DOI and a verbatim quote or an explicit evidence locator. Over this shared claim store, AskChem exposes complementary structures for search and synthesis: a stabilized faceted taxonomy for hierarchical retrieval and browsing, an evidence graph linking claims through relations, and an exploratory living taxonomy that situates indexed papers under scientific principles. AskChem currently indexes 2.4M claims from 147K papers and provides a web interface, as well as REST, SDK, and MCP access for AI agents. On AskChem-Bench, grounding a GPT-5.5 reader in AskChem yields 100% resolvable DOIs, compared with 88.3% without retrieval, and the highest citation density among five tested systems. AskChem is live at https://askchem.org.