arXiv:2608. 05798v1 Announce Type: cross Abstract: Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation.
By Andrey Shutkin, Denis Parkhomenko, Ivan Kirillov, Kirill Chernyshev, Kirill Malakhov, Ilia Vasiliev, Ilia Trushkin, Valeriya Kobenko, David Chikovani, Alexander Ivanov, Azat Saginbaev, Egor Silvestrov, Ivan Mikheev, Konstantin Zakharov
arXiv:2610.00686v1 Announce Type: new
Abstract: Recent video-based world models pair the scalability of autoregressive (AR) prediction with the visual quality of diffusion models. The choice of scene...
By Mikhail Dereviannykh, Vikram Voleti, Simon Donne, Mallikarjun Byrasandra Ramalinga Reddy, Shimon Vainer, Mark Boss
arXiv:2608. 08794v1 Announce Type: new Abstract: Omni-modal LLMs jointly process audio, video, and text, but long multimodal sequences incur substantial prefill and KV-cache costs.
By Kyeongyoon Lee, Hongyeob Kim, Youngeun Kim, Sungeun Hong
arXiv:2511. 20973v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate token sequences (e.
By Saurabhchand Bhati, Samuel Thomas, Hilde Kuehne, Rogerio Feris, James Glass
arXiv:2606. 02739v1 Announce Type: cross Abstract: Audio tokenizers serve as the discrete interface between continuous audio and Audio Language Models (ALMs), but existing tokenizers often struggle to support both understanding and generation.
By Hui Li, Yangfan Gao, Junlin Shang, Changhao Jiang, Tao Gui, Qi Zhang, Xuanjing Huang
arXiv:2606. 06357v1 Announce Type: cross Abstract: Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio encoders capture semantics but are not directly decodable.
By Dinghao Zhou, Xingchen Song, Di Wu, Pengyu Cheng, Shengfan Shen, Sixiang Lv