发表机构
New York University; Matterstack, Inc.（纽约大学; Matterstack公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AskChem是一种以主张为中心的跨论文化学搜索基础设施，将检索单元改为带来源的主张，提供多种检索结构与访问接口，在基准测试中提升了DOI可解析率，为化学文献综合提供了支持。
AI 中文摘要
化学文献综合通常需要整合分散在众多出版物中的特定发现，但现有文献搜索系统主要返回排名后的文档列表。因此，科学家和智能体需要手动定位相关信息、验证其来源并整合跨论文的答案。我们提出AskChem，一种面向跨论文化学搜索的以主张为中心的基础设施。AskChem将检索单元从论文转变为带有来源的主张：每篇论文被转换为原子化、有类型的主张，每个主张都有来源DOI、逐字引用或显式证据定位器作为基础。在这个共享的主张库之上，AskChem提供了用于搜索和综合的互补结构：用于分层检索和浏览的稳定分面分类法、通过关系链接主张的证据图，以及将索引论文置于科学原理下的探索性动态分类法。AskChem目前索引了来自14.7万篇论文的240万条主张，并提供网页界面以及REST、SDK和MCP接口供智能体使用。在AskChem-Bench上，将GPT-5.5阅读器与AskChem结合后，DOI可解析率达到100%，而不使用检索时为88.3%，且在五个测试系统中拥有最高的引用密度。AskChem已上线，网址为此https URL。
英文摘要
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.
