Component Ablation for Efficient Hybrid Language Model Architectures: Performance, Resilience, and Compression Implications 事件

Component Ablation for Efficient Hybrid Language Model Architectures: Performance, Resilience, and Compression Implications arXiv:2603.22473v2 Announce Type: replace-cross Abstract: Hybrid language models combine softmax attention with linear-time sequence mechanisms such as state-space or linear-attention layers, but the functional contribution of each component type remains insufficiently characterized. We study component-level ablation in two sub-1B hybrid language models, Qwen3.5-0.8B and F