Zyphra introduces ZHA hierarchical attention, a sparse method with subquadratic routing

Zyphra introduced Zyphra Hierarchical Attention (ZHA), a sparse attention method that builds a tree of compressed KV-cache summaries and routes queries top-down through it, giving subquadratic complexity with suitable top-p thresholds. Existing pretrained models can be adapted via a short KL-distillation phase using only a few billion tokens; on Qwen3.6-35B-A3B, ZHA adds 81,920 trainable parameters, about 0.00024% of the base model. On RULER-13 at 256K tokens the wide-tree model scored 96.2% versus 96.0% for dense attention, and the deep six-level tree scored 63.4% on LongBench v2 versus 58.0% for dense while attending to about 1.1% of the KV cache; a GPU roofline model projects 32-35x speedup over dense attention at 10M tokens.