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This paper proposes Error-Subspace Calibration (ESCal), a post-merging calibration framework that identifies low-dimensional error subspaces from weight differences between expert and merged models, using lightweight Bridge modules to recover task-specific behavior. Experiments on vision and NLU benchmarks show ESCal improves merged-model performance across diverse architectures and merging methods.