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Tversky Neural Networks

Authors: Moussa Koulako Bala Doumbouya, Dan Jurafsky, Christopher D. Manning Paper: https://arxiv.org/abs/2506.11035 Once a year, some interesting architecture inevitably appears where they change some fundamental building block. This happened with KAN last year, where they changed the parameterization of the neuron activation function (though it's unclear what the outcome is after a year — many follow-up works seem to have appeared, but KANs haven't displaced anyone anywhere yet). The same is

Muvera: Making multi-vector retrieval as fast as single-vector search

Neural embedding models have become a cornerstone of modern information retrieval (IR). Given a query from a user (e.g., “How tall is Mt Everest?”), the goal of IR is to find information relevant to the query from a very large collection of data (e.g., the billions of documents, images, or videos on the Web). Embedding models transform each datapoint into a single-vector “embedding”, such that semantically similar datapoints are transformed into mathematically similar vectors. The embeddings are