collaborators

6 papers

cs.CV2026

Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models

Enyi Shi, Pengyang Shao, Yanxin Zhang +5

The robust safety of Vision-Language Large Models (VLLMs) against joint multilingual and multimodal threats remains severely underexplored. Current benchmarks typically isolate the…

cs.CL2026

FreeAct: Freeing Activations for LLM Quantization

Xiaohao Liu, Xiaobo Xia, Manyi Zhang +6

Quantization is pivotal for mitigating the significant memory and computational overhead of Large Language Models (LLMs). While emerging transformation-based methods have successfu…

cs.IR2026

CLEAR: Null-Space Projection for Cross-Modal De-Redundancy in Multimodal Recommendation

Hao Zhan, Yihui Wang, Yonghui Yang +6

Multimodal recommendation has emerged as an effective paradigm for enhancing collaborative filtering by incorporating heterogeneous content modalities. Existing multimodal recommen…

cs.CL2026

Transport and Merge: Cross-Architecture Merging for Large Language Models

Chenhang Cui, Binyun Yang, Fei Shen +5

Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data, yet many real-world deployments rely on smaller models trained or adapted from…

cs.MM2026

A Survey on Cross-Modal Interaction Between Music and Multimodal Data

Sifei Li, Mining Tan, Feier Shen +5

Multimodal learning has driven innovation across various industries, particularly in the field of music. By enabling more intuitive interaction experiences and enhancing immersion,…

cs.CV2026

Who Transfers Safety? Identifying and Targeting Cross-Lingual Shared Safety Neurons

Xianhui Zhang, Chengyu Xie, Linxia Zhu +6

Multilingual safety remains significantly imbalanced, leaving non-high-resource (NHR) languages vulnerable compared to robust high-resource (HR) ones. Moreover, the neural mechanis…