A new continual‑learning method, Selective Forgetting‑Aware Optimization, uses cosine‑based gradient gating to prevent catastrophic forgetting while preserving memory efficiency. By projecting, accepting, or discarding updates per layer, the approach balances plasticity and stability across tasks.
The Signal
Benchmarks show competitive accuracy with lower memory costs, offering a scalable solution for dynamic AI systems worldwide.