AdaRank: Adaptive Rank Pruning for Enhanced Model Merging

Researchers introduce AdaRank, a new AI model merging framework that adaptively selects optimal singular directions from task vectors to combine multiple fine-tuned models. The technique addresses cross-task interference issues in existing SVD-based approaches by dynamically pruning problematic components during test-time, achieving state-of-the-art performance with nearly 1% gap from individual fine-tuned models.