5 papers
When Routing Collapses: On the Degenerate Convergence of LLM Routers
Guannan Lai, Han-Jia Ye
LLM routing aims to achieve a favorable quality--cost trade-off by dynamically assigning easy queries to smaller models and harder queries to stronger ones. However, across both un…
MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing
Haoxuan Ma, Guannan Lai, Han-Jia Ye
Multimodal large language models (MLLMs) have advanced rapidly, yet heterogeneity in architecture, alignment strategies, and efficiency means that no single model is uniformly supe…
Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping
Guannan Lai, Yujie Li, Xiangkun Wang +3
Class Incremental Learning (CIL) aims to enable models to learn new classes sequentially while retaining knowledge of previous ones. Although current methods have alleviated catast…
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge Transfer
Yujie Li, Guannan Lai, Xin Yang +3
Open-World Continual Learning (OWCL) is a challenging paradigm where models must incrementally learn new knowledge without forgetting while operating under an open-world assumption…
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
Guannan Lai, Yihui Feng, Xin Yang +5
Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address chal…