We wanted a scenario where, say, 5 well-placed border points could efficiently represent an area with 5,000 internal points and 10,000 road edges. This would reduce those 10,000 edges to just 5*4/2 = 10 shortcuts for routing through that cluster at a high level – an incredible 1:1000 point ratio and a 30x reduction in edges to consider for the high-level path!
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具体来看，Qwen3.5 采用混合注意力机制，结合高稀疏的 MoE 架构创新，并基于更大规模的文本和视觉混合 Token 上训练，Qwen3.5-122B-A10B 与 Qwen3.5-35B-A3B 以更小的总参数和激活参数量，实现了更大的性能提升。