2 papers
cs.CV2026
MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios
Haitian Wang, Ruoxi Sun, Quantong Qiu +5
Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context windows do not necessarily translate…
cs.IR2026
Compute Only Once: UG-Separation for Efficient Large Recommendation Models
Hui Lu, Zheng Chai, Shipeng Bai +15
Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behaviors, but this trend also leads to prohib…