@inproceedings{huynh-etal-2026-efficient,
title = "Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval",
author = "Huynh, Tuan-Luc and
Wang, Weiqing and
Le, Trung and
Vu, Thuy-Trang and
Gasevic, Dragan and
Li, Yuan-Fang and
Do, Thanh-Toan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.249/",
pages = "5677--5701",
ISBN = "979-8-89176-459-0",
abstract = "Retrievers are a key bottleneck in temporal Retrieval-Augmented Generation (RAG) systems: failing to retrieve temporally relevant context can degrade downstream generation, regardless of LLM reasoning. We propose Temporal-aware Matryoshka Representation Learning (TMRL), an efficient method that equips retrievers with temporal-aware Matryoshka embeddings. TMRL leverages the nested structure of Matryoshka embeddings to introduce a temporal subspace, enhancing temporal encoding while preserving general semantic representations. Experiments show that TMRL efficiently adapts diverse standard encoder-only text embedding models, achieving competitive temporal retrieval and temporal RAG performance compared to existing Matryoshka-based adaptation and temporal retrieval methods, while enabling flexible accuracy-efficiency trade-offs."
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<abstract>Retrievers are a key bottleneck in temporal Retrieval-Augmented Generation (RAG) systems: failing to retrieve temporally relevant context can degrade downstream generation, regardless of LLM reasoning. We propose Temporal-aware Matryoshka Representation Learning (TMRL), an efficient method that equips retrievers with temporal-aware Matryoshka embeddings. TMRL leverages the nested structure of Matryoshka embeddings to introduce a temporal subspace, enhancing temporal encoding while preserving general semantic representations. Experiments show that TMRL efficiently adapts diverse standard encoder-only text embedding models, achieving competitive temporal retrieval and temporal RAG performance compared to existing Matryoshka-based adaptation and temporal retrieval methods, while enabling flexible accuracy-efficiency trade-offs.</abstract>
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%0 Conference Proceedings
%T Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval
%A Huynh, Tuan-Luc
%A Wang, Weiqing
%A Le, Trung
%A Vu, Thuy-Trang
%A Gasevic, Dragan
%A Li, Yuan-Fang
%A Do, Thanh-Toan
%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 huynh-etal-2026-efficient
%X Retrievers are a key bottleneck in temporal Retrieval-Augmented Generation (RAG) systems: failing to retrieve temporally relevant context can degrade downstream generation, regardless of LLM reasoning. We propose Temporal-aware Matryoshka Representation Learning (TMRL), an efficient method that equips retrievers with temporal-aware Matryoshka embeddings. TMRL leverages the nested structure of Matryoshka embeddings to introduce a temporal subspace, enhancing temporal encoding while preserving general semantic representations. Experiments show that TMRL efficiently adapts diverse standard encoder-only text embedding models, achieving competitive temporal retrieval and temporal RAG performance compared to existing Matryoshka-based adaptation and temporal retrieval methods, while enabling flexible accuracy-efficiency trade-offs.
%U https://aclanthology.org/2026.emnlp-main.249/
%P 5677-5701
Markdown (Informal)
[Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval](https://aclanthology.org/2026.emnlp-main.249/) (Huynh et al., EMNLP 2026)
ACL