arXiv AI

Multimodal Music Recommendation System using LLMs

arXiv:2606. 00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content.

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

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation

arXiv:2509. 09685v5 Announce Type: replace-cross Abstract: We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline.

By Keunwoo Choi, Seungheon Doh, Juhan Nam

llms agents multimodal data-engineering

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

Jul 9

MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations

arXiv:2607. 06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments.

By Sirui Zhang, Tianle Wang, Xinyi Tong, Peiyang Yu, Jishang Chen, Liangke Zhao, Haoxin Zhang, Duo Xu, Xin Jin, Feng Yu, Songchun Zhu

multimodal benchmarks

arXiv AI

Jul 7

EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation

arXiv:2508. 16170v2 Announce Type: replace-cross Abstract: MultiModal Recommendation (MMR) systems have emerged as a promising solution for improving recommendation quality by leveraging rich item-side modality information, prompting a surge of diverse methods.

By Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng, Yongjie Wang, Zhiqi Shen

llms efficiency multimodal safety

arXiv Machine Learning

Aug 3

GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System

arXiv:2607. 29213v1 Announce Type: cross Abstract: Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effective fusion of these heterogeneous modalities remains challenging, hindering both the joint modeling of multimodal signals and adaptation to evolving user intent.

By Jiping Liu, Zhongmin Zhang, Zisen Sang, Zhijia Fang, Tao Ouyang, Ma Jiang, Shaopeng Liang, Zeyang Hou, Guodong Cao, Jia Jia

rag reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 28

Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering

arXiv:2511. 23304v2 Announce Type: replace Abstract: In this paper, we propose a novel Multi-Modal Scene Graph with Kolmogorov-Arnold Expert Network for Audio-Visual Question Answering (SHRIKE).

By Zijian Fu, Changsheng Lv, Xianlin Zhang, Mengshi Qi, Huadong Ma

nlp multimodal benchmarks

arXiv Machine Learning

Jul 31

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

arXiv:2607. 27909v1 Announce Type: cross Abstract: Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes.

By Dmitrii Gavrilev, Ilya Borovik, Vladimir Viro

rag safety

arXiv AI

Jun 15

AudioDER: A Deduplication-Enhanced Reasoning Dataset for Post-Training Large Audio-Language Models

arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.

By Hui Geng, Yi Su, Han Yin, Tianjiao Wan, Qisheng Xu, Jiaxin Chen, Zijian Gao, Hengzhu Liu, Xie Chen, Kele Xu

llms benchmarks

arXiv AI

Jun 9

APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music

arXiv:2605. 03395v2 Announce Type: replace-cross Abstract: Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems.

By Jaavid Aktar Husain, Dorien Herremans

rag

arXiv AI

Jun 9

Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis

arXiv:2606. 07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues.

By Pawe{\l} Pozorski, Jakub Muszy\'nski, Maria Ganzha

llms nlp multimodal safety

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.

By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao

llms rag multimodal benchmarks safety

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