NExRI: Neural Explicit Rule Induction for Abstract Visual Reasoning

Abstract visual reasoning requires inducing a relation from a few observed instances and re-applying it to novel ones. Existing models encode this relation implicitly in network weights, leaving it unobservable and unverifiable. We introduce **NExRI** (**N**eural **Ex**plicit **R**ule **I**nduction), a framework for end-to-end explicit rule induction on neural features, realized by two coupled modules. *IVaR* constructs contextual states that expose the latent relation by predicting the third panel from the first two and iteratively refining the prediction-observation discrepancy through verify-and-refine updates. *RaXO* turns these states into explicit rule objects that are validated on unseen evidence and executed on candidate completions. Instantiated as polynomial equations, the induced rules keep the fit tractable and the whole framework differentiable end-to-end. Experiments demonstrate state-of-the-art performance across multiple abstract visual reasoning benchmarks.