Geometric Iterative Retrieval for Neural Audio Codec Resynthesis
This work argues that this dichotomy of resynthesis as a choice between discrete token prediction and continuous regression is incomplete and introduces geometric iterative retrieval, a paradigm that uses the RVQ layer hierarchy itself as a natural iterative decomposition in continuous codebook space.