The Bicameral Model: Bidirectional Hidden-State Coupling Between Parallel Language Models

The Bicameral Model couples two frozen, pretrained language models via a trainable neural interface on their intermediate hidden states, allowing them to operate in lockstep. This method enables a primary model to drive a task while an auxiliary model uses tools or solves constraints, significantly improving accuracy on tasks like arithmetic and logic puzzles.