Neural network weight symmetry explains only part of shared-vs-independent fitting gap

A researcher analyzed ~1.8M fitted SIREN neural networks (sine-based implicit representations) to separate three claims about parameter symmetry: that symmetry exists, that accounting for it improves weight-space prediction, and that it fully explains why reading semantics from weights works with shared initialization but fails with independent fitting.