A new arXiv paper introduces SPICE, a technique that applies clustering to explain polysemantic features in neural networks, where individual neurons respond to multiple unrelated concepts. The approach aims to make functional interpretation of such neurons clearer for interpretability research. The abstract describes the work as a simple method for clustering-based explanation of these overlapping activations.
A new arXiv preprint describes an approach that derives logical explanations for neural network classifications by analyzing neuron activations. The work situates itself within formal explainability, which aims to give provable guarantees about model behavior across regions of the input space. The abstract notes that existing formal techniques have limitations the proposed method seeks to address, though details of the approach are not included in the announcement.
A revised arXiv paper argues that feature learning in neural networks is not fully captured by the prevailing view that networks converge on a single global low-dimensional representation. The authors point to neuron specialization inside multilayer perceptrons as an additional, separate mechanism through which features are acquired and organized. The work aims to broaden the theoretical picture of how networks structure what they learn.