| title | Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Caches |
|---|---|
| authors | Vivek Kalyanarangan |
| date | 2026-09-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.17652 |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and the scan that ranks all n keys for a top-k step becomes the traffic that bounds decoding. We present Fathom, a key scan in which each query decides how many bits of each key channel to read. The 4-bit K cache is stored channel-major as bit planes, so a prefix of t planes is exactly the channel's t-bit quantizer, and the query spends its bit budget by reverse water-filling over the variance-weighted importance of its channels. At one million tokens on Qwen3-8B a decode step is 1.67x faster in GPU time than with the 136-bit scans of Double Sparsity, Loki and SparQ r=32, and in the same GPU time as SparQ's 68-bit read (r=16) Fathom reads 18% fewer bytes with lower attention error on six of seven model and context settings. On RULER-style tasks every per-token scan matches exact top-k decoding, and on real coding-agent sessions Fathom reaches the step agreement of the most accurate 136-bit scan at 92 bits. The store is the 4-bit K copy a quantized serving stack already holds, and the method is not faster when the index is resident in GPU memory. |
cite
@misc{kalyanarangan2026fathom,
title={Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Caches},
author={Vivek Kalyanarangan},
year={2026},
eprint={2609.17652},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.17652}
}