Systematic analysis assesses the memory patterns in transformer models, indicating strategies for cache compression.
Systematic analysis of attention memory patterns in transformer-based large language models, examining head specialization, attention sink phenomena, information density gradients across layers, and key-value redundancy patterns that inform cache compression strategies.
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Oleh Ivchenko (2026) studied this question.
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