AnalysisAI ModelsOctober 2, 2026

Two papers tackle catastrophic forgetting in continual learning

Read original source →arxiv.org

Local Support Learning frames forgetting as a geometric problem in each weight matrix's input space, arguing gradient-based updates are suboptimal under a natural retention objective. A second paper unifies data attribution, forgetting, and plasticity loss as a recurring update cycle for models trained throughout their lifetime.

2 sources

More stories today

Open the live feed