pith:BWIAXPTY
Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
Quest selects only the top-K critical KV cache pages using query vectors and min-max key bounds to accelerate long-context LLM attention.
arxiv:2406.10774 v2 · 2024-06-16 · cs.CL · cs.LG
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Claims
By only loading the Top-K critical KV cache pages for attention, Quest significantly speeds up self-attention without sacrificing accuracy. We show that Quest can achieve up to 2.23x self-attention speedup, which reduces inference latency by 7.03x while performing well on tasks with long dependencies with negligible accuracy loss.
That the min/max key approximation per page, combined with query-vector scoring, reliably identifies the truly critical pages without dropping information that would change the final attention output on long-dependency tasks.
Quest speeds up long-context LLM self-attention by up to 2.23x via query-dependent selection of top-K critical KV cache pages, cutting overall latency by 7.03x with negligible accuracy loss.
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| First computed | 2026-05-17T23:38:52.375607Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0d900bbe7828ac19169ae7f91c49846449e053ca56781d809c9ea37308fe874f
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/BWIAXPTYFCWBSFU2474RYSMEMR \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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