AMOR: Entropy-Gated SSM-Attention Hybrid
AMOR is a hybrid model inspired by dual-process cognition theories, dynamically activating sparse attention only when SSM predictions show high entropy uncertainty. It projects Ghost KV from SSM states for O(n) efficiency, outperforming SSM-only and Transformer baselines on retrieval tasks with perfect accuracy using just 22% attention positions. Prediction entropy reliably detects retrieval needs with a 1.09 nats gap.







