研究团队提出几何迭代检索方法，解决了神经音频编码器重合成难题，效果比之前单次预测和回归方法好。
基于残差向量量化（RVQ）的神经音频编解码器存在音频令牌重合成的难题；传统采用离散令牌预测与连续回归的方式存在局限；提出几何迭代检索方法，利用码书空间对比检索实现重合成；在语音和音乐领域的测试中，效果优于单次令token预测和一步回归基线。
Geometric Iterative Retrieval for Neural Audio Codec Resynthesis
Neural audio codecs based on Residual Vector Quantization (RVQ) have become the dominant discrete representation for token-based general audio generation, yet resynthesizing high-quality audio from coarse codec tokens remains an open problem and bounds the fidelity of every system that generates them. Prior work has framed resynthesis as a choice between discrete token prediction and continuous regression. We argue that this dichotomy is incomplete and introduce geometric iterative retrieval, a paradigm that uses the RVQ layer hierarchy itself as a natural iterative decomposition in continuous codebook space. Rather than classifying over discrete vocabularies or regressing to a single target vector, our method performs contrastive retrieval in the codebook's geometric space. We evaluate our method on codec restoration tasks across speech and music, and show improvements over both single-pass token prediction and one-step regression baselines.