Still: Amortized KV Cache Compaction in a Single Forward Pass
The paper presents Still, a lightweight per-layer Perceiver that compacts KV caches in a single forward pass for long‑horizon language model inference. It demonstrates superior speed‑quality trade‑offs across a range of compression ratios and context lengths on models such as Qwen and Gemma. The method also improves summarization performance, surpassing strong baselines like KV‑Distill on benchmarks including RULER and LongBench.
- ▪The KV cache is identified as the primary memory bottleneck for deploying long‑horizon language models.
- ▪Existing compaction approaches either lack expressiveness (selection methods) or require per‑context optimization (synthesis methods).
- ▪Still trains a small per‑layer Perceiver once against a frozen base model to generate compact keys and values in a single forward pass.
- ▪Experiments on Qwen and Gemma models show Still achieving favorable speed‑quality results across 8× to 200× compression and 8k‑128k context lengths.
- ▪On the RULER benchmark, Still outperforms the strongest baseline by 8–22 points and wins a LongBench summarization comparison against KV‑Distill.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.07878 |
| Publication time | Sun, 14 Jun 2026 22:29:07 +0000 |
| Retrieval time | 2026-06-14T22:37:33.112Z |
| Last seen | 2026-06-14T22:37:33.112Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | dH6jq7fH_EW0 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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 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 View PDF HTML (experimental) 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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