arXiv: cs.AI preprintsInternational6 November 2026
TriAttention: Unified Sparse Mixed-Precision Attention for Long-Context LLM Inference
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arXiv:2611.07256v1 Announce Type: cross Abstract: Sparsity and quantization reduce the cost of attention by skipping interactions and lowering the precision of those retained. However, useful interactions differ in their precision requirements: some tolerate low-precision scores while remaining sensitive to removal. We therefore introduce TriAttention, which assigns query-key (QK) tiles to skipping, 4-bit, or 8-bit computation, using 4-bit to retain quantization-tolerant interactions at lower cost. To represent compactly, we encode each head's computation regions with two diagonal-distance bou
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