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CoWindow Attention（CoWA）：让完整因果覆盖成为注意力头集合的集体属性
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研究者提出结构化注意力架构 CoWA，将因果历史访问分散到各 KV head：所有 head 共享近对角与 prefix-sink 窗口，互补的长程窗口划分其余历史，其并集实现完整因果覆盖，且无需学习型 router 或 indexer。
正文
Abstract:FullAttn repeatedly exposes the complete causal history to every attention head, creating substantial redundant computation and memory traffic even with IO-efficient dense kernels. We introduce CoWA, a structured attention architecture that distributes access to the causal history across KV heads. All heads share near-diagonal and prefix-sink windows, while complementary long-range windows partition the remaining history. Their union provides full causal coverage although each head attends sparsely to distant tokens. This position-defined attention pattern requires no learned router or indexer, is used consistently during training and inference, and aligns with KV-head tensor parallelism. A window-matched ablation at 8K isolates the effect of complementary long-range allocation: CoWA with 100% collective coverage reaches 89.73% accuracy, compared with 89.97% for FullAttn, while duplicated long-range windows perform substantially worse. Across a broader controlled associative-recall comparison with matched token budgets, CoWA closely tracks FullAttn as the context grows, whereas other sparse patterns lose a substantial fraction of the associations. In an attention-operator benchmark at 128K tokens with tensor parallelism, CoWA reduces forward and backward latency during training by 7.4x and 8.6x and decoding latency during inference by 3.0x over FullAttn. Its per-rank peak operator memory matches FullAttn during training and is 7.6x lower during decoding. Across scaling-law training from 0.6B to 14B parameters, CoWA closely tracks FullAttn in perplexity while reducing total training FLOPs. The resulting 14B models and 32B models from separate continued training achieve comparable knowledge, reasoning, and long-context retrieval scores to FullAttn. These results show that full causal coverage can be a collective property of the head ensemble rather than a duplicated property of every head.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.32704 [cs.AI] |
| (or arXiv:2609.32704v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.32704 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jingze Shi [view email]
[v1]
Sat, 26 Sep 2026 15:09:07 UTC (503 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org