GRAM: Group-wise Rank-Aware Model Merging (arXiv:2610.29110v1)
This paper introduces GRAM, a method that groups geometrically compatible task updates using principal-angle geometry and then applies an SVD-based merge within each group using only fine-tuned weights. GRAM reduces a failure mode called 'subspace interference' and outperforms prior model-merging techniques on benchmarks including GLUE with Flan-T5, an 8-task CLIP vision suite, and medical instruction-tuned Qwen2-7B-Instruct with LoRAs.