Functional compatibility as a determinant of persistent neural learning
Neural networks can acquire new capabilities while damaging existing ones, but what determines whether new learning persists remains unclear. We identify functional compatibility, the extent to which incoming learning can coexist with behaviour that must be preserved, as an experimentally manipulable causal determinant of persistence. From identical neural states, we vary compatibility while matching unrestricted learning opportunity and imposing a common retention requirement. Persistent learning increases with compatibility across independent directions, convolutional and transformer architectures, vision and text, and a ten-seed replication. Learning rules and retention constraints det...
| Authors | Hossein Javidnia |
|---|---|
| Primary category | cs.LG |
| Published | 23 August 2026 |
| Last updated at source | 29 September 2026 |
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