The parameters in weight-sparse transformers are interpretable

Understanding what individual components of a neural network do is a central goal of mechanistic interpretability, and a valuable tool for auditing or editing trained models. The dominant approach picks a behavior and reverse-engineers the circuit that implements it. This gives a local picture, since we learn what a weight does in service of one task on one distribution, but not what it does in general. We therefore ask the reverse question. Can we pick any single nonzero parameter in a trained transformer and say what it does, without first choosing a behavior? Until recently, this was out of reach, because the weight basis of a standard transformer is not aligned with the computations the network performs (Elhage, et al. 2022). However, weight-sparse transformers (Gao et al. 2025) offer an alternative path forward. By training most weights to zero, the surviving weights align more cleanly with computation, which opens the door to per-weight interpretation. In this work, we introduce an automated LLM-pipeline that produces a short, human-readable account of when a given weight matters, verifies the account on held-out data, and applies it at scale to compare two weight-sparse transformers against two dense controls. Empirically, we find that a significant percentage of nonzero weights (17-35%) on sparse transformers are interpretable, compared to 5-9% on the equivalent dense model and Pythia-70m. Together these results suggest that sparse training produces transformers whose individual weights are interpretable to a degree not achievable in conventional transformers, providing a potential starting point for per-weight interpretation of larger models