Functional compatibility as a determinant of persistent neural learning
Quick summary
arXiv:2608.22462v2 Announce Type: replace-cross Abstract: 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 ind
Key takeaways
- arXiv:2608.22462v2 Announce Type: replace-cross Abstract: 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.
Why it matters
“Functional compatibility as a determinant of persistent neural learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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