#linear complexity

MemAgent introduces a reinforcement learning-based memory agent that allows large language models to process ultra-long documents efficiently, maintaining high accuracy with linear computational costs.

RWKV-X introduces a hybrid model combining sparse attention and recurrent memory to enable efficient decoding of extremely long sequences with linear complexity, outperforming previous RWKV models on long-context tasks.