Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructions
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Abstract (arXiv)
A query-oblivious coreset for a softmax-attention head is a subset of the key-value pairs whose attention output is within varepsilon of the full one for every query in a ball. Liberty, Andoni and Kleiner proved that unweighted coresets of size O(√ d eρ+frac12logρ+o(loglogρ)/varepsilon) exist, ρ the query radius times the centred key radius, against a lower bound Omega(√ d eρ/varepsilon), and conjectured that closing the gap needs new techniques. It does not: a spherical lift of both balls into one exponential-kernel instance lets the Bozzai-Rothvoss chaining bound apply, and Chevet's inequality splits key from value dimension, giving coresets of size O(eρ(√dv+√dklog(1+ρ))/varepsilon) in randomised polynomial time, the first constructive whole-ball guarantee within √log(1+ρ) of the lower bound. A sampling cap O(e2ρ/varepsilon2) completes the envelope; in fixed dimension Tai's diameter-free bound removes the logarithm, settling the Gaussian-restriction case of a Bozzai-Rothvoss question for the kernels. We give theLiberty-Andoni-Kleiner lower boud transfer the one-waycommunication bounds of Chen et r is the price of one signing forall queries. A census of every head of Qwen2.5-7B-Instruct and Llama-3-8B-Instruct finds ρ at least 23.877, so everyactor eρ/varepsilonprescribes a coreset larger than the cache: the algorithmic contribution is asymptotic on these models.
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References · 24
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2024Qwen2.5 Technical Reportcited by 4,713
- 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
- 2024SnapKV: LLM Knows What You are Looking for Before Generationcited by 848
- 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
- 2021Kernel Thinningcited by 47
- 2018Near-Optimal Coresets of Kernel Density Estimatescited by 30
- 2019Discrepancy, Coresets, and Sketches in Machine Learningcited by 30
- 2020Discrepancy Minimization via a Self-Balancing Walkcited by 22
- 2024SubGen: Token Generation in Sublinear Time and Memorycited by 21
- 2017The Gram-Schmidt Walk: A Cure for the Banaszczyk Bluescited by 9
- 2025Streaming Attention Approximation via Discrepancy Theorycited by 9
12 more
- 2014Spectral norm of random tensorscited by 8
- 2020Optimal Coreset for Gaussian Kernel Density Estimationcited by 6
- 2026WildCat: Near-Linear Attention in Theory and Practicecited by 6
- 2024A Quasi-Monte Carlo Data Structure for Smooth Kernel Evaluationscited by 5
- 2025Compression Barriers for Autoregressive Transformerscited by 5
- 2026Nearly Optimal Attention Coresetscited by 4
- 2026The risk of KV cache compressioncited by 4
- 2025Token Sample Complexity of Attentioncited by 3
- 2026How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformerscited by 3
- 2023Stronger Coreset Bounds for Kernel Density Estimators via Chainingcited by 1
- 2026Towards Tight Bounds for Streaming Attentioncited by 1
- 2026Intrinsic Interaction Geometry Controls the Low-Rank Complexity of Softmax Attentioncited by 1
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