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The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends

This survey explores the evolution of attention mechanisms in large language models, focusing on memory management strategies such as explicit-memory compression, sparse access, and recurrent state construction. It introduces a five-dimensional framework to analyze how contextual memory is represented, updated, accessed, read, and integrated within models, highlighting trends in architectural design and memory coordination across different layers and functions.

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PublishedSeptember 30, 2026Zhentao Tan, Jingyi Shen, Yanbo Li
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This research provides a comprehensive analysis of attention mechanisms in LLMs, offering insights into memory management strategies and architectural trends that can inform future model design and optimization.

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