AMOR: Entropy-Gated SSM-Attention Hybrid

What Changed

Dynamically routes to attention only on high-entropy SSM positions

Why It Matters

AMOR enables efficient hybrid architectures that adapt compute to task difficulty, potentially slashing inference costs for long-context LLMs. It bridges SSM speed with Transformer precision, advancing scalable AI models. Researchers can build more interpretable systems with metacognitive routing.

What To Do Next

Download arXiv:2602.13215 and replicate AMOR on synthetic retrieval benchmarks.