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    <abstract>Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simply feeding more documents into the context window fails to capture how evidence should be connected. We address this gap with thought templates, reusable reasoning patterns derived from prior problem solving that structure how evidence is combined and guide multi-hop inference alongside factual documents. To keep these templates effective, we propose an update strategy that iteratively refines templates derived from training data through natural-language feedback. Across diverse benchmarks and LCLM families, our approach delivers consistent gains over strong baselines in both retrieval-based and retrieval-free settings. Furthermore, we show that optimized templates can be distilled into relatively smaller open-source models, demonstrating its broad applicability. We refer to our framework as Thought Template Augmented LCLMs (ToTAL).</abstract>
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%0 Conference Proceedings
%T When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
%A Jeong, Soyeong
%A Jung, Taehee
%A Hwang, Sung Ju
%A Kim, Joo-Kyung
%A Kang, Dongyeop
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jeong-etal-2026-thoughts
%X Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simply feeding more documents into the context window fails to capture how evidence should be connected. We address this gap with thought templates, reusable reasoning patterns derived from prior problem solving that structure how evidence is combined and guide multi-hop inference alongside factual documents. To keep these templates effective, we propose an update strategy that iteratively refines templates derived from training data through natural-language feedback. Across diverse benchmarks and LCLM families, our approach delivers consistent gains over strong baselines in both retrieval-based and retrieval-free settings. Furthermore, we show that optimized templates can be distilled into relatively smaller open-source models, demonstrating its broad applicability. We refer to our framework as Thought Template Augmented LCLMs (ToTAL).
%U https://aclanthology.org/2026.findings-acl.81/
%P 1625-1646

Markdown (Informal)

[When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs](https://aclanthology.org/2026.findings-acl.81/) (Jeong et al., Findings 2026)

• When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs (Jeong et al., Findings 2026)

ACL

• Soyeong Jeong, Taehee Jung, Sung Ju Hwang, Joo-Kyung Kim, and Dongyeop Kang. 2026. When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1625–1646, San Diego, California, United States. Association for Computational Linguistics.