Structural Abstraction as an Inductive Bias for Non-Stationary Language Model Training 事件

Structural Abstraction as an Inductive Bias for Non-Stationary Language Model Training arXiv:2603.17198v2 Announce Type: replace-cross Abstract: A foundational principle in cognitive science holds that intelligent agents do not learn by storing experiences as isolated instances, but by forming abstract schemas that capture relational structure shared across situations. Even though this claim is well supported by behavioral and neuroimaging studies, its role as a computational training signal in