3 papers
cs.IR2026
Reasoning-Augmented Representations for Multimodal Retrieval
Jianrui Zhang, Anirudh Sundara Rajan, Brandon Han +3
Universal Multimodal Retrieval (UMR) seeks any-to-any search across text and vision, yet modern embedding models remain brittle when queries require latent reasoning (e.g., resolvi…
cs.CV2025
See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models
Le Thien Phuc Nguyen, Zhuoran Yu, Samuel Low Yu Hang +8
Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely assess fine-grained reasoning about hum…
cs.LG2025
Contamination Detection for VLMs using Multi-Modal Semantic Perturbation
Jaden Park, Mu Cai, Feng Yao +3
Recent advances in Vision-Language Models (VLMs) have achieved state-of-the-art performance on numerous benchmark tasks. However, the use of internet-scale, often proprietary, pret…