  Recent research has shown that combining Mamba with Transformer architecture,
which has selective state space and quadratic self-attention mechanism,
outperforms using Mamba or Transformer architecture alone in language modeling
tasks. The quadratic self-attention mechanism effectively alleviates the
shortcomings of selective state space in handling long-term dependencies of any
element in the sequence. We propose a position information injection method
that connects the selective state space model with the quadratic attention, and
integrates these two architectures with hybrid experts with cross-sharing
domains, so that we can enjoy the advantages of both. We design a new
architecture with a more biomimetic idea: Observer-Thinker-Conceiver-Expresser
(OTCE), which can compete with well-known medium-scale open-source language
models on a small scale in language modeling tasks.
