Hybrid Architectures for Language Models: Systematic Analysis and Design Insights

ArXi:2510.04800v2 Announce Type: replace Recent progress in large language models nstrates that hybrid architectures--combining self-attention mechanisms with structured state space models like Mamba--can achieve a compelling balance between modeling quality and computational efficiency, particularly for long-context tasks. While these hybrid models show promising performance, systematic comparisons of hybridization strategies and analyses on the key factors behind their effectiveness have not been clearly shared to the community.