New Hybrid Architecture LPC-SM Separates Attention, Memory, and Prediction for Efficient Long-Context Language Models

Researchers propose LPC-SM, a hybrid autoregressive architecture that decomposes long-context modeling into local attention, persistent memory, and predictive correction rather than relying solely on attention mechanisms. Testing on a 158M-parameter model shows the approach maintains stability at 4096-token sequences while improving language modeling loss, suggesting attention-alternative decompositions can enhance efficiency in long-context AI systems.