新学习能否持久，取决于它与已有功能的兼容性。
Functional compatibility as a determinant of persistent neural learning
- 通过调节功能兼容性，控制新旧能力共存程度。
- 兼容性越高，新学习越能长期保留，跨模型架构均有效。
- 为理解学习持久性提供新视角，适合神经网络稳定性研究者。
神经网络在获得新能力时常会损害原有功能，但新学习能否持久仍不明确。我们发现功能兼容性——即新学习与必须保留行为的共存程度——是可实验操控的因果决定因素。在相同神经状态基础上，我们保持学习机会不变且施加统一保留要求，仅改变兼容性。结果显示，在独立方向、卷积与Transformer架构、视觉与文本任务中，持久学习随兼容性提升而增加，并在十组种子重复实验中一致。学习规则和保留约束决定可保留的兼容机会量，而非线性几何则限制了大更新范数下的匹配干预。因此，功能兼容性将稳定-可塑性问题从防止遗忘，转变为决定哪些新学习能与现有功能共存并持久。
原文摘要 · Abstract (English)
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 determine how much compatible opportunity is retained, whereas nonlinear geometry limits the matched intervention at larger update norms. Functional compatibility therefore reframes stability-plasticity from preventing forgetting to determining which new learning can coexist with existing function and persist.
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