3 papers
cs.LG2026
Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation
Wei Zhang, Lin Tang, Ming Zhao +1
Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hu…
cs.CL2026
DAIN: Dynamic Agent-Based Interaction Network for Efficient and Collaborative Multimodal Reasoning
Xinxin Chen, Yuchen Li, Zihan Wang +3
Current multimodal fusion approaches, particularly those based on static Mixture-of-Experts (MoE) architectures, often struggle to provide the adaptive and efficient collaborative…
cs.LG2026
Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates
Lin Tang, Wei Zhang, Jing Li +3
Low-rank adaptation (LoRA) makes it cheap to train many domain- and task-specific language model adapters, but whether two adapters can be merged is usually discovered only after b…