arXiv AI

MusTBench: Benchmarking and Advancing Temporal Grounding in Music LLMs

MusTBench is a music‑expert‑validated benchmark that evaluates temporal grounding in Large Audio‑Language Models (LALMs) through five temporally grounded question‑answering tasks. The paper also introduces MusT, a four‑stage optimization recipe—music encoder adaptation, LLM adaptation, supervised fine‑tuning, and RL‑based optimization—to improve temporal grounding. Experiments show that current LALMs struggle with precise temporal grounding, while MusT yields significant improvements, highlighting temporal grounding as a key missing capability in these models.

arXiv AI

6d ago

TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models

TEMPO is a unified model that adds temporally‑grounded capabilities to large audio‑language models, enabling timestamping of events, speakers, and sounds in audio, speech, and music. It introduces a supervised fine‑tuning stage featuring atomic timestamp tokens, a time‑aware projector with sinusoidal encodings, and a distance‑aware Gaussian loss, trained via a synthetic‑to‑real curriculum. Additionally, TEMPO employs reinforcement learning (GRPO) as a refinement step, and achieves state‑of‑the‑art performance on a benchmark of 10K samples across five timestamping tasks, surpassing Audio Flamingo Next and Qwen3‑Omni.

By Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh, Utathya Aich, Ramani Duraiswami, Dinesh Manocha

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 7

Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

arXiv:2607. 04383v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level precision only over a closed label set.

By Zihan Zhang, Xize Cheng, Wenhao Yan, Tong Zhang, Dongjie Fu, Boyun Zhang, Yongbo He, Tao Jin

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

MOSS-Audio Technical Report

arXiv:2606. 01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning.

By Chen Yang, Chufan Yu, Hanfu Chen, Jie Zhu, Jingqi Chen, Ke Chen, Wenxuan Wang, Yang Wang, Yaozhou Jiang, Yi Jiang, Zhengyuan Lin, Ziqi Chen, Zhaoye Fei, Chenghao Liu, Jun Zhan, Kang Yu, Kexin Huang, Mingshu Chen, Qinyuan Cheng, Ruixiao Li, Shimin Li, Songlin Wang, Yang Gao, Yiyang Zhang, Xipeng Qiu

llms agents nlp fine-tuning multimodal

arXiv AI

Jul 1

Beyond Binary Instrument QA: Probing Instrument Grounding in Music Audio-Language Models

arXiv:2606. 31338v1 Announce Type: cross Abstract: Recent music audio-language models achieve high accuracy on instrument question-answering benchmarks, but it remains unclear whether this reflects robust audio grounding or benchmark-specific shortcuts.

By Yujun Lee, Joonhyeok Shin, Hyoeun Kim, Kyuhong Shim

llms benchmarks safety

arXiv AI

Jun 24

Event-Grounded Question Answering over Long Audio via Structured Retrieval

arXiv:2602. 14612v4 Announce Type: replace-cross Abstract: Answering natural-language questions over multi-hour audio requires both event recognition and temporal grounding.

By Kartik Hegde, Arvind Krishna Sridhar, Naveen Vakada, Yinyi Guo, Erik Visser

llms rag nlp benchmarks data-engineering

arXiv Machine Learning

Aug 11

Assessing Factual Music Comprehension in Large Audio Language Models

arXiv:2511. 05550v3 Announce Type: replace-cross Abstract: Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio.

By Daniel Chenyu Lin, Michael Freeman, John Thickstun

llms multimodal benchmarks

arXiv AI

Jun 3

SegTune: Structured and Fine-Grained Control for Song Generation

arXiv:2606. 02638v1 Announce Type: cross Abstract: Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts.

By Yuejiao Wang, Zihao Ji, Pengfei Cai, Xu Li, Haorui Zheng, Zewen Song, Zhongliang Liu, Chen Zhang, Pengfei Wan

llms diffusion safety data-engineering

arXiv AI

Jun 18

Continuous Audio Thinking for Large Audio Language Models

arXiv:2606. 18273v1 Announce Type: cross Abstract: Large audio language models (LALMs) have shown impressive capabilities on diverse audio understanding tasks, ranging from speech transcription to music analysis.

By Gyojin Han, Dong-Jae Lee, Changho Choi, Jongsuk Kim, Junmo Kim

llms efficiency benchmarks

arXiv Machine Learning

Jun 17

A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models

arXiv:2606. 17417v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception.

By Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh, Sarah Wiegreffe, Dinesh Manocha, Ramani Duraiswami

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 8

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation

arXiv:2606. 07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages.

By Yun-Chen Cheng, Tzu-Hung Huang, Chih-Pin Tan

benchmarks safety

arXiv AI

Jul 14

MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models

arXiv:2603. 09714v2 Announce Type: replace-cross Abstract: While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored.

By Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo, Ping-Le Tsai, Yen-Ting Piao, Hung-Wei Chen, Ting-Lin Hsiao, Yun-Man Hsu, Ke-Han Lu, Hung-yi Lee

llms benchmarks

arXiv AI

Jul 16

Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models

arXiv:2607. 13408v1 Announce Type: cross Abstract: Recent text-to-audio models generate high-quality audio, but often fail to follow instructions involving multiple sound events and temporal order.

By Chun-Yi Kuan, Siwon Kim, Byeonggeun Kim, Suyoun Kim, Bo-Ru Lu, Qinming Tang, Ankur Gandhe, Hung-yi Lee, Chieh-Chi Kao, Chao Wang

llms benchmarks

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