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    <abstract>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, trace each finding to its source, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure that changes the retrieval unit for cross-paper chemistry search from the paper to the source-linked claim. Available source text is converted into atomic, typed claims, each carrying a source DOI and a verbatim quote or an explicit evidence locator. Over this shared claim store, AskChem exposes complementary operational structures for search and synthesis: a corpus-induced faceted taxonomy stabilized for hierarchical retrieval and browsing, an automatically extracted evidence graph linking claims through typed relations, and an exploratory living taxonomy that situates indexed papers under scientific principles. At the frozen index snapshot, AskChem contains 2.4M claims from 147K papers, 30% processed from full text and 70% from abstracts only. The system provides a web interface, as well as REST, SDK, and MCP access for AI agents. On a 30-question AskChem-Bench pilot focused on citation traceability and paper relevance rather than factual-answer accuracy, 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.</abstract>
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%0 Conference Proceedings
%T AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
%A Yan, Bing
%A Wolfe, Gregory
%A Martiniani, Stefano
%A Cho, Kyunghyun
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F yan-etal-2026-askchem
%X 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, trace each finding to its source, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure that changes the retrieval unit for cross-paper chemistry search from the paper to the source-linked claim. Available source text is converted into atomic, typed claims, each carrying a source DOI and a verbatim quote or an explicit evidence locator. Over this shared claim store, AskChem exposes complementary operational structures for search and synthesis: a corpus-induced faceted taxonomy stabilized for hierarchical retrieval and browsing, an automatically extracted evidence graph linking claims through typed relations, and an exploratory living taxonomy that situates indexed papers under scientific principles. At the frozen index snapshot, AskChem contains 2.4M claims from 147K papers, 30% processed from full text and 70% from abstracts only. The system provides a web interface, as well as REST, SDK, and MCP access for AI agents. On a 30-question AskChem-Bench pilot focused on citation traceability and paper relevance rather than factual-answer accuracy, 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.
%U https://aclanthology.org/2026.emnlp-demos.69/
%P 728-737

Markdown (Informal)

[AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis](https://aclanthology.org/2026.emnlp-demos.69/) (Yan et al., EMNLP 2026)

• AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis (Yan et al., EMNLP 2026)

ACL

• Bing Yan, Gregory Wolfe, Stefano Martiniani, and Kyunghyun Cho. 2026. AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 728–737, Budapest, Hungary. Association for Computational Linguistics.