Measuring the Representational Alignment of Neural Systems in Superposition

ArXi:2604.00208v1 Announce Type: new Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns. However, neural systems frequently operate in superposition, encoding features than they have neurons via linear compression.