Long Context Modeling with Ranked Memory-Augmented Retrieval
Ghadir Alselwi, Hao Xue The Hong Kong University of Science and Technology, Shoaib Jameel University of Southampton, Basem Suleiman University of New South Wales, Flora D. Salim, Imran Razzak Mohamed bin Zayed University of Artificial Intelligence
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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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表