CTkvr: KV Cache Retrieval for Long-Context LLMs via Centroid then Token Indexing
Published:Dec 17, 2025 15:56
•1 min read
•ArXiv
Analysis
This article introduces CTkvr, a novel approach for efficiently retrieving KV caches in long-context LLMs. The method utilizes a two-stage process: first, identifying relevant centroids, and then indexing tokens within those centroids. This could potentially improve the performance and scalability of LLMs dealing with extensive input sequences. The paper's focus on KV cache retrieval suggests an effort to optimize the memory access patterns, which is a critical bottleneck in long-context models. Further evaluation is needed to assess the practical impact and efficiency gains compared to existing methods.
Key Takeaways
- •CTkvr is a new method for KV cache retrieval in long-context LLMs.
- •It uses a two-stage process: centroid identification and token indexing.
- •The approach aims to improve performance and scalability for long input sequences.
- •Focuses on optimizing memory access patterns, a key bottleneck in long-context models.
Reference
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