Attention Is Not All You Need: The Newcomen Architecture for Persistent Conversational State

This paper proposes the Newcomen Architecture, a hybrid system combining transformer-based large language models with recurrent neural networks and vector-based memory stores to address fundamental limitations in current LLM implementations; specifically the absence of persistent conversational state, episodic memory, and emotional continuity. The architecture maps to neuroscientific structures: the transformer as cortex (interpretation), the RNN as hypothalamus/thalamus (state and salience), and vector databases as hippocampus (memory). We present a complete implementation blueprint using off-the-shelf components and note convergent independent work from Google Research's Titans architecture, which arrives at a similar tri-component decomposition through mathematical formalism. The Newcomen Architecture offers a practical retrofit path for augmenting existing deployed LLMs with persistent state and memory.