Researchers have introduced SFAO, a selective gradient projection technique that mitigates catastrophic forgetting in neural networks. By regulating update directions through cosine similarity and per‑layer gating, the method balances learning new tasks with preserving past knowledge.
The Signal
Experiments on global continual‑learning benchmarks show competitive accuracy while drastically reducing memory usage, promising more efficient AI deployment worldwide.