arXiv:2604.15773v1 Announce Type: cross
Abstract: We introduce a novel characterization of phase transitions based on hypothesis testing.
In our formulation, a phase transition is defined as the breakdown of statistical indistinguishability under vanishing parameter perturbations in the thermodynamic limit.
This perspective provides a general, order-parameter-free framework that does not rely on model-specific insights or learning procedures.
We show that conventional approaches, such as those based on the Binder parameter, can be reinterpreted as special cases within this framework.
As a concrete realization, we employ a distribution-free two-sample run test and demonstrate that the critical point of the two-dimensional Ising model is accurately identified without prior knowledge of the order parameter.
Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers
arXiv:2604.20027v1 Announce Type: cross Abstract: For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially from human attentional