Balancing Memory Pathways: Analyzing and Improving Memory Utilization in Hybrid LMs

Excerpt

arXiv:2610.06750v1 Announce Type: cross Abstract: Recurrent-attention hybrid language models (LMs), which interleave attention and recurrent layers, are increasingly used to combine the efficiency of the recurrent layers with the strong performance of attention layers. Prior work suggests that attention and recurrent layers offer complementary pathways to use past information: attention supports precise memory recall from earlier tokens, while recurrent layers support consolidation of disparate