@inproceedings{nam-etal-2026-timelyrag,
title = "{T}imely{RAG}: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents",
author = "Nam, Youngeun and
Kim, Joeun and
Song, Hwanjun and
Yoon, Susik and
Lee, Jae-Gil and
Lee, Byung Suk",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.398/",
pages = "8414--8430",
ISBN = "979-8-89176-462-0",
abstract = "Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments. Existing time-sensitive retrieval methods address only the *disjoint*-evolving environment, where each update is an independent snapshot. However, laws, policies, and regulations often operate in *overlapping*-evolving environments, where amendments override earlier clauses while preserving most content, creating strong semantic overlap across versions. We propose ***TimelyRAG***, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. We also introduce ***TimelyQABench***, the first benchmark for regulation-heavy domains with overlapping-evolving challenges. Experiments show consistent gains, up to +28.6{\%} in nDCG@10, highlighting the importance of temporal reasoning for reliable QA over evolving documents. All resources are available at https://github.com/kaist-dmlab/TimelyRAG."
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<abstract>Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments. Existing time-sensitive retrieval methods address only the *disjoint*-evolving environment, where each update is an independent snapshot. However, laws, policies, and regulations often operate in *overlapping*-evolving environments, where amendments override earlier clauses while preserving most content, creating strong semantic overlap across versions. We propose ***TimelyRAG***, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. We also introduce ***TimelyQABench***, the first benchmark for regulation-heavy domains with overlapping-evolving challenges. Experiments show consistent gains, up to +28.6% in nDCG@10, highlighting the importance of temporal reasoning for reliable QA over evolving documents. All resources are available at https://github.com/kaist-dmlab/TimelyRAG.</abstract>
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%0 Conference Proceedings
%T TimelyRAG: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents
%A Nam, Youngeun
%A Kim, Joeun
%A Song, Hwanjun
%A Yoon, Susik
%A Lee, Jae-Gil
%A Lee, Byung Suk
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F nam-etal-2026-timelyrag
%X Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments. Existing time-sensitive retrieval methods address only the *disjoint*-evolving environment, where each update is an independent snapshot. However, laws, policies, and regulations often operate in *overlapping*-evolving environments, where amendments override earlier clauses while preserving most content, creating strong semantic overlap across versions. We propose ***TimelyRAG***, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. We also introduce ***TimelyQABench***, the first benchmark for regulation-heavy domains with overlapping-evolving challenges. Experiments show consistent gains, up to +28.6% in nDCG@10, highlighting the importance of temporal reasoning for reliable QA over evolving documents. All resources are available at https://github.com/kaist-dmlab/TimelyRAG.
%U https://aclanthology.org/2026.findings-emnlp.398/
%P 8414-8430
Markdown (Informal)
[TimelyRAG: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents](https://aclanthology.org/2026.findings-emnlp.398/) (Nam et al., Findings 2026)
ACL