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    <abstract>LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals. Humanities scholarship, however, requires interpretive, evidence-grounded arguments over primary sources, whose value rests on faithful quotation, verifiable provenance, and deep interpretation. Existing research agents use general planning, tool use, and reflection, leaving these scholarly operations implicit. To address this gap, we introduce **SPIRE** (**S**cholarly-**P**rimitives-**I**nspired **R**esearch **E**ngine), a multi-agent framework that realizes Scholarly Primitives, a typology of basic humanities scholarship practices, as cooperating roles over a multi-scale close-reading substrate of passages, intra-context graph communities, and cross-context semantic clusters. On a benchmark of peer-reviewed papers in classical Chinese and Greco-Roman Latin scholarship, SPIRE recovers substantially more cited primary-source evidence and receives higher blind human and LLM ratings on four scholarly quality dimensions than LLM, RAG, and generic agentic baselines. Retrieval-tier and agent ablations, with retrieval-volume and answer-length controls, trace its advantage to targeted retrieval, structured evidence selection, and claim-evidence binding. Code and benchmark data are released at https://github.com/YatingPan/SPIRE.</abstract>
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%0 Conference Proceedings
%T Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship
%A Pan, Yating
%A Zhang, Jiajun
%A Wang, Jun
%A Su, Qi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F pan-etal-2026-extending
%X LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals. Humanities scholarship, however, requires interpretive, evidence-grounded arguments over primary sources, whose value rests on faithful quotation, verifiable provenance, and deep interpretation. Existing research agents use general planning, tool use, and reflection, leaving these scholarly operations implicit. To address this gap, we introduce **SPIRE** (**S**cholarly-**P**rimitives-**I**nspired **R**esearch **E**ngine), a multi-agent framework that realizes Scholarly Primitives, a typology of basic humanities scholarship practices, as cooperating roles over a multi-scale close-reading substrate of passages, intra-context graph communities, and cross-context semantic clusters. On a benchmark of peer-reviewed papers in classical Chinese and Greco-Roman Latin scholarship, SPIRE recovers substantially more cited primary-source evidence and receives higher blind human and LLM ratings on four scholarly quality dimensions than LLM, RAG, and generic agentic baselines. Retrieval-tier and agent ablations, with retrieval-volume and answer-length controls, trace its advantage to targeted retrieval, structured evidence selection, and claim-evidence binding. Code and benchmark data are released at https://github.com/YatingPan/SPIRE.
%U https://aclanthology.org/2026.emnlp-main.1396/
%P 31893-31916

Markdown (Informal)

[Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship](https://aclanthology.org/2026.emnlp-main.1396/) (Pan et al., EMNLP 2026)

• Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship (Pan et al., EMNLP 2026)

ACL

• Yating Pan, Jiajun Zhang, Jun Wang, and Qi Su. 2026. Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31893–31916, Budapest, Hungary. Association for Computational Linguistics.