AMOR: Entropy-Gated SSM-Attention Hybrid
π‘Hybrid SSM-Transformer hits perfect accuracy with 78% less attention compute.
β‘ 30-Second TL;DR
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.
Key Points
- β’Dynamically routes to attention only on high-entropy SSM positions
- β’Achieves perfect retrieval accuracy with 22% attention usage
- β’Uses Ghost KV projection from SSM for O(n) compute efficiency
- β’Entropy signals retrieval need with 1.09 nats gap over local positions
- β’Provides interpretable adaptive computation via information theory
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Original source: ArXiv AI β
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