arXiv AI June 10, 2026 By Chien-Chun Wang, Hung-Shin Lee, Hsin-Min Wang, Berlin Chen

DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment

arXiv:2606. 10010v1 Announce Type: cross Abstract: Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion scores (MOS).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 12

CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

arXiv:2603. 00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind.

By Yinghao Ma, Haiwen Xia, Hewei Gao, Weixiong Chen, Yuxin Ye, Yuchen Yang, Sungkyun Chang, Mingshuo Ding, Yizhi Li, Ruibin Yuan, Simon Dixon, Emmanouil Benetos

reinforcement-learning multimodal benchmarks safety

arXiv AI

Aug 25

Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation

arXiv:2608.23484v1 Announce Type: new Abstract: We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a mu...

By Naman Garg, Sarika Jain, George Fazekas

llms rag multimodal

Hugging Face Trending Papers

Aug 24

Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation

We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a multi-modal and personalized conversational recomme...

llms rag multimodal

arXiv AI

Jun 16

TuneJury: An Open Metric for Improving Music Generation Preference Alignment

arXiv:2606. 17006v1 Announce Type: cross Abstract: We introduce TuneJury, an open, instance-level pairwise reward model for text-to-music that predicts a music preference score from a text prompt and an audio clip.

By Yonghyun Kim, Junwon Lee, Haiwen Xia, Yinghao Ma, Junghyun Koo, Koichi Saito, Yuki Mitsufuji, Chris Donahue

reinforcement-learning benchmarks safety

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

Sep 16

MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions

MUUNRiver-Bench is a diagnostic benchmark for music retrieval that uses natural‑language instructions to define relevance for reference‑audio queries. It contains 3,440 tracks across 13 genres and 116 sub‑genres and covers seven tasks such as similar‑music, style‑preserving lyric‑rewriting, cover, and segment retrieval. Experiments with six models in eight configurations show that acoustic encoders favor local identity while text‑aligned encoders favor semantic relations, and that instruction‑aware and audio‑text fusion systems do not consistently outperform their backbones.

By Zhancheng Guo, Congren Dai, Shangda Wu, Jianhuai Hu, Danni Zhao, Xiaobing Li, Maosong Sun

llms multimodal benchmarks

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CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation

Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation

TuneJury: An Open Metric for Improving Music Generation Preference Alignment

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

MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions