Long Context Modeling with Ranked Memory-Augmented Retrieval

Ghadir Alselwi; Hao Xue; Shoaib Jameel; Basem Suleiman; Flora D. Salim; Imran Razzak

Long Context Modeling with Ranked Memory-Augmented Retrieval

Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Flora D. Salim, Imran Razzak

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Abstract

Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augmented Retrieval ERMAR framework, which dynamically ranks memory entries based on relevance. Unlike prior models, ERMAR employs a novel relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. By integrating historical usage patterns and adaptive retrieval, ERMAR achieves state-of-the-art results on standard benchmarks, demonstrating superior scalability and performance in long-context tasks.

Anthology ID:: 2026.acl-long.162
Volume:: Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:: July
Year:: 2026
Address:: San Diego, California, United States
Editors:: Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:: ACL
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 3576–3590
Language:
URL:: https://aclanthology.org/2026.acl-long.162/
DOI:
Bibkey:
Cite (ACL):: Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Flora D. Salim, and Imran Razzak. 2026. Long Context Modeling with Ranked Memory-Augmented Retrieval. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3576–3590, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):: Long Context Modeling with Ranked Memory-Augmented Retrieval (Alselwi et al., ACL 2026)
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PDF:: https://aclanthology.org/2026.acl-long.162.pdf
Checklist:: 2026.acl-long.162.checklist.pdf

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@inproceedings{alselwi-etal-2026-long,
    title = "Long Context Modeling with Ranked Memory-Augmented Retrieval",
    author = "Alselwi, Ghadir  and
      Xue, Hao  and
      Jameel, Shoaib  and
      Suleiman, Basem  and
      Salim, Flora D.  and
      Razzak, Imran",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.162/",
    pages = "3576--3590",
    ISBN = "979-8-89176-390-6",
    abstract = "Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augmented Retrieval ERMAR framework, which dynamically ranks memory entries based on relevance. Unlike prior models, ERMAR employs a novel relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. By integrating historical usage patterns and adaptive retrieval, ERMAR achieves state-of-the-art results on standard benchmarks, demonstrating superior scalability and performance in long-context tasks."
}

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%0 Conference Proceedings
%T Long Context Modeling with Ranked Memory-Augmented Retrieval
%A Alselwi, Ghadir
%A Xue, Hao
%A Jameel, Shoaib
%A Suleiman, Basem
%A Salim, Flora D.
%A Razzak, Imran
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F alselwi-etal-2026-long
%X Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augmented Retrieval ERMAR framework, which dynamically ranks memory entries based on relevance. Unlike prior models, ERMAR employs a novel relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. By integrating historical usage patterns and adaptive retrieval, ERMAR achieves state-of-the-art results on standard benchmarks, demonstrating superior scalability and performance in long-context tasks.
%U https://aclanthology.org/2026.acl-long.162/
%P 3576-3590

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Markdown (Informal)

[Long Context Modeling with Ranked Memory-Augmented Retrieval](https://aclanthology.org/2026.acl-long.162/) (Alselwi et al., ACL 2026)

Long Context Modeling with Ranked Memory-Augmented Retrieval (Alselwi et al., ACL 2026)

ACL

Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Flora D. Salim, and Imran Razzak. 2026. Long Context Modeling with Ranked Memory-Augmented Retrieval. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3576–3590, San Diego, California, United States. Association for Computational Linguistics.