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20242026
most citedLLMs Meet Multimodal Generation and Editing: A Survey

2 citations · 6 across the 15 of their papers we have counts for

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cs.SD2026

WorldSonus: Bringing Sound to Worlds

Pengjun Fang, Jingyi Fa, Kam Man Wu +9

Recent advances in world models have enabled increasingly realistic visual synthesis. However, these generated environments remain largely silent. Bringing sound to world models po…

cs.SD2026

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Ziyang Ma, Zhikang Niu, Wenming Tu +30

We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To sup…

cs.SD2026

AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation

Zeyue Tian, Lei Ke, Zhaoyang Liu +8

Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework,…

cs.SD2026★ 1 cited

Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing

Zeyue Tian, Binxin Yang, Zhaoyang Liu +8

Recent progress in multimodal models has spurred rapid advances in audio understanding, generation, and editing. However, these capabilities are typically addressed by specialized…

cs.SD2025

Inference-time Scaling for Diffusion-based Audio Super-resolution

Yizhu Jin, Zhen Ye, Zeyue Tian +4

Diffusion models have demonstrated remarkable success in generative tasks, including audio super-resolution (SR). In many applications like movie post-production and album masterin…

cs.SD2025

Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound

Liumeng Xue, Ziya Zhou, Jiahao Pan +20

Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs). However, audio understanding and gene…