The paper introduces a geometry-aware hyperbolic residual quantization method for variational autoencoders, addressing inconsistencies that arise when extending residual vector quantization to hyperbolic space. It restores telescoping behavior in the forward pass via Hyperbolic Residual Aggregation and improves gradient flow in the backward pass with a discounted Hyperbolic Straight-Through Estimator. Experiments on hierarchical prediction, recommendation, image tokenization, and neural audio coding demonstrate enhanced stability and structural organization of hyperbolic residual codes, while highlighting a trade‑off between compression efficiency and hierarchical organization.
By Alessio Colombo, Melika Ayoughi
arXiv:2609.39451v1 Announce Type: new
Abstract: Progressive autoregressive image codecs provide an appealing paradigm for generative compression by quantizing continuous latents into discrete tokens,...
By Qin Yan, Ruixiao Dong, Yutao Xie, Li Li, Ying Chen, Kai Li, Daowen Li, Houqiang Li
RGSQ introduces a Riemannian geometry‑aware post‑training quantization method for large vision‑language models, treating quantization as a reconstruction problem under a Fisher‑Riemannian metric. It identifies modality‑specific sensitive directions via manifold mappings and applies geometry‑aligned rotations and whitening to steer low‑bit perturbations toward loss‑insensitive axes. Experiments on diverse VLM benchmarks show RGSQ delivers the best accuracy and stability in extremely low‑bit settings, outperforming existing VLM‑aware baselines by up to 5.9% and single‑modality methods by up to 8.6%.
By Zhiping Wu, Dongdong Ren, Yangchengyu Zhou, Zhengjie Zhang, Wenbin Li, Hongbing Pan, Yang Gao
The paper introduces geometric iterative retrieval, a new approach for resynthesizing high‑quality audio from coarse Residual Vector Quantization (RVQ) codec tokens. Instead of choosing between discrete token prediction or continuous regression, the method performs contrastive retrieval within the continuous codebook space, leveraging the RVQ hierarchy as an iterative decomposition. Experiments on speech and music codec restoration tasks demonstrate that this technique outperforms both single‑pass token prediction and one‑step regression baselines.
By Leo Schmidt-Traub, Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Roger Wattenhofer
arXiv:2607.14088v2 Announce Type: replace
Abstract: Video generation models typically rely on 3D-VAEs trained for pixel-level reconstruction, whose latent spaces may underrepresent semantic structure...
By Zhihao Xie, Junfeng Wu, Xinting Hu, Junchao Huang, Li Jiang
arXiv:2609.17509v1 Announce Type: cross
Abstract: Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing comput...
By Thanapat Trachu, Samuele Cornell, William Chen, Shinji Watanabe