AI RESEARCH

Guaranteed Optimal Compositional Explanations for Neurons

arXiv CS.AI

ArXi:2511.20934v2 Announce Type: replace Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and concepts through logical rules, typically computed via a search over all possible concept combinations. Since computing the spatial alignment over the entire state space is computationally infeasible, the literature commonly adopts assumptions related to the structure of the combinations and beam search to restrict the state space.