Research·arXiv cs.LG·May 25

From Latent Space to Training Data: Explainable Specialization in Minimal MLPs

Researchers demonstrate that training regularization can force individual neurons in minimal MLPs to specialize into interpretable prototypes, enabling faithful reconstruction of training data from learned weights. The work bridges neural network interpretability and mechanistic understanding by showing that structural losses promoting neuron coverage and separation outperform standard fitting across controlled experiments. This advances the emerging field of reverse-engineering what networks learn, with implications for auditing model behavior and understanding how architectural constraints shape learned representations.

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Explainer

The key novelty is showing that regularization can force neurons to become faithful prototypes of training data rather than distributed representations, and that this reconstruction fidelity actually improves generalization. Prior interpretability work has focused on post-hoc explanation; this demonstrates that interpretability can be baked into training as a structural objective.

This connects directly to the emerging consensus around architectural inductive bias we saw in the WaveLiT paper on neural PDE solvers. Both argue that carefully designed structural constraints (here, coverage regularization; there, wavelet tokenization) can outperform brute-force approaches. The minimal MLP work also echoes the causal methods paper's argument that rigor and interpretability matter alongside raw performance. Where those stories focused on efficiency and reasoning quality, this one targets auditability: if you can reconstruct what a network memorized, you can verify its behavior in safety-critical contexts.

If the same coverage regularization approach scales to networks larger than minimal MLPs (say, 100M+ parameters) while maintaining both interpretability and generalization, that confirms the principle generalizes beyond toy settings. If it doesn't, the work remains a controlled-experiment proof-of-concept rather than a practical auditing tool. Watch whether follow-up papers attempt this scaling within the next 6-9 months.

Coverage we drew on

Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers · arXiv cs.LG

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MentionsGaussian-activation MLPs · prototype-based reconstruction · coverage regularization

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