[2606.07878] Still: Amortized KV Cache Compaction in a Single Forward Pass

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Computer Science > Machine Learning

arXiv:2606.07878 (cs)

[Submitted on 5 Jun 2026]

Title:Still: Amortized KV Cache Compaction in a Single Forward Pass

Authors:Charles O'Neill, Alex Sandomirsky, Harry Partridge, Mudith Jayasekara, Max Kirkby<br>View a PDF of the paper titled Still: Amortized KV Cache Compaction in a Single Forward Pass, by Charles O'Neill and 4 other authors

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Abstract:The KV cache is the memory bottleneck of long-horizon language model deployment. Practically, a deployable compactor must be lightweight enough to call during inference, expressive enough to preserve context under constraint, and reusable across a trajectory. Existing compaction methods satisfy only part of this requirement: selection methods are lightweight but subset-bound, while synthesis methods are expressive but rely on per-context optimization. Here we introduce Still, a small per-layer Perceiver trained once against a frozen base model that produces compact keys and values in a single forward pass. On Qwen and Gemma models, Still occupies the favorable side of the speed--quality frontier across compression ratios from $8\times$ to $200\times$ and context lengths from $8$k to $128$k. On the long-context RULER grid, Still exceeds the strongest baseline by 8--22 points. The same compact cache also supports free-form summarization, preserving most of the full-context gain on HELMET and winning a pairwise LongBench summarization comparison against KV-Distill. Because compaction is a forward pass, Still can be applied iteratively, entering a long-horizon regime unavailable to per-context methods. We show that amortization makes long-context cache compaction tractable, and synthesis makes its compact state useful at extreme compression.

Subjects:

Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.07878 [cs.LG]

(or<br>arXiv:2606.07878v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.07878

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arXiv-issued DOI via DataCite

Submission history<br>From: Charles O'Neill [view email]<br>[v1]<br>Fri, 5 Jun 2026 22:21:23 UTC (1,481 KB)

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