Augmenting Attention with Exponentially Decaying Memory Improves Query-Aware KV Sparsity

Xiuying Wei, Caglar Gulcehre

Augmenting Attention with Exponentially Decaying Memory Improves Query-Aware KV Sparsity: 0 upvotes on Hugging Face Daily Papers, #46 of 48 papers on 2026-06-08. Day-by-day upvote history.

Efficient inference is critical for long-context language models, where attention computation and KV-cache access dominate the cost. Recent work RAT+, introduces a recurrence-augmented attention backbone that enables flexible dilated attention at inference time. In this paper, we investigate whether this exponentially decaying memory can also improve existing query-aware sparse inference methods. Using representative methods including Quest, MoBA, and SnapKV, we show that RAT+ consistently improves accuracy over standard attention across sparse budgets on eight needle-in-a-haystack tasks. We validate these gains both on the released checkpoints from the RAT+ paper and on OLMo2-7B, which we continue pretraining with the added memory module for 10B tokens. Finally, we propose two hypotheses explaining why this memory module benefits query-aware sparse inference and design targeted experiments to support them.

Paper page on Hugging Face · arXiv

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