![[2602.16284] Fast KV Compaction via Attention Matching](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16284] Fast KV Compaction via Attention Matching
Summary
The paper presents a novel approach for fast key-value (KV) compaction via Attention Matching, addressing the challenges of scaling language models with long contexts while minimizing quality loss.
Why It Matters
As language models grow, managing long contexts efficiently is crucial for performance. This research offers a solution that enhances KV cache compaction speed without significantly sacrificing output quality, which is vital for real-world applications in natural language processing.
Key Takeaways
- Introduces Attention Matching for efficient KV cache compaction.
- Achieves up to 50x compaction speed with minimal quality loss.
- Decomposes complex problems into simpler subproblems for efficient solutions.
- Addresses the limitations of traditional summarization methods in managing long contexts.
- Pushes the boundaries of performance in language model applications.
Computer Science > Machine Learning arXiv:2602.16284 (cs) [Submitted on 18 Feb 2026] Title:Fast KV Compaction via Attention Matching Authors:Adam Zweiger, Xinghong Fu, Han Guo, Yoon Kim View a PDF of the paper titled Fast KV Compaction via Attention Matching, by Adam Zweiger and 3 other authors View PDF HTML (experimental) Abstract:Scaling language models to long contexts is often bottlenecked by the size of the key-value (KV) cache. In deployed settings, long contexts are typically managed through compaction in token space via summarization. However, summarization can be highly lossy, substantially harming downstream performance. Recent work on Cartridges has shown that it is possible to train highly compact KV caches in latent space that closely match full-context performance, but at the cost of slow and expensive end-to-end optimization. This work describes an approach for fast context compaction in latent space through Attention Matching, which constructs compact keys and values to reproduce attention outputs and preserve attention mass at a per-KV-head level. We show that this formulation naturally decomposes into simple subproblems, some of which admit efficient closed-form solutions. Within this framework, we develop a family of methods that significantly push the Pareto frontier of compaction time versus quality, achieving up to 50x compaction in seconds on some datasets with little quality loss. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.16284 [cs.LG] ...
