arXiv cs.LGPaper
Predicting and Repairing Merge Collapse in Large Language Models
Model merging is becoming operational reality for builders deploying multiple specialized models. This offers a diagnostic you can run before merging to avoid silent failure, which beats evaluating a bad merge after the fact. The 12-out-of-14 prediction accuracy on held-out merges is credible. If you're orchestrating multiple fine-tuned specialists, this is useful immediately.