Stream-CQSA: Exact Out-of-Memory Recovery for Attention
Long-context large language models are limited not only by attention cost but also by out-of-memory (OOM) failures. A selected attention call may not fit in available device memory even when the kernel is optimized. Exact and approximate attention methods reduce memory use, but every fixed implementation still has a device-specific capacity boundary. We introduce Stream-CQSA, an attention-level OOM recovery framework based on CQS decomposition, derived from the theory of cyclic quorum sets (CQS). Stream-CQSA recursively partitions an infeasible attention call into independent subsequence tasks, executes each with a compatible inner kernel, and recomposes the local statistics to recover th...
| Authors | Yiming Bian, Joshua M. Akey |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 1 September 2026 |
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