There Will Be a Scientific Theory of Deep Learning [R]

**A 14-author perspective paper (arXiv:2604.21691) argues that a coherent scientific theory of deep learning is emerging, organizing evidence across five categories: solvable toy models, insightful limits, empirical scaling laws, hyperparameter theories (e.g., μP), and universal phenomena observed across architectures.** The authors draw explicit analogies to how physics developed theoretical frameworks before full mathematical formalization, suggesting these converging lines of evidence constitute early-stage but legitimate scientific theory rather than ad hoc observations. For ML practitioners and researchers, this framing matters because it proposes a structured research agenda — prioritizing mechanistic understanding over benchmark chasing — and could influence how the field allocates effort between empirical scaling work and theoretical investigation.