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CatKV: Accelerating Position-Independent Caching with Context-Adaptive KV Cache Compression

Summary: CatKV addresses remote KV-transfer bottlenecks in position-independent caching with context-adaptive SVD and latent-space quantization, tuned to each chunk’s spectrum. CPU-offloaded compression, grouped factorization, and pipelining yield 2–5× faster TTFT and 6–10× higher compression without accuracy loss. (summarized by gpt-6-luna on Oct 22 2026)

Paper ID
h8e4ce2118452e5e3
Venue
SIGMOD
Year
2027
Pagerank
4.9605343e-05
Overall Rank
10,389 | 30.37%
DOI
10.1145/3837104
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BibTeX Citation

@inproceedings{xu_sigmod27,
        title = {{CatKV: Accelerating Position-Independent Caching with Context-Adaptive KV Cache Compression}},
        author = {Xu, Jie and Liu, Renjie and Li, Yi and Tang, Bo},
        series = {{SIGMOD} '27},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3837104},
        url = {https://dl.acm.org/doi/10.1145/3837104},
        year = {2027}
}

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