SDHCE extracts formulas from trained MLPs

This is a genuinely interesting interpretability project because it does not just explain a model after the fact — it tries to compress the model into a smaller symbolic program you can inspect and keep.

• –The strongest hook is the “distillation check,” which tests whether the named concept layer alone can reproduce the network rather than stopping at a pretty explanation
• –The autonaming system is more ambitious than basic neuron labeling because it traces signed contributions back to raw features and cancels opposing paths
• –Today the evidence is still narrow: the public repo centers on Iris and a small MLP, so the big question is whether the method survives noisier real-world datasets
• –As an open-source repo instead of a hosted product, this will appeal more to interpretability researchers and experimental ML hackers than to everyday app developers