Merge Behavior, Not Just Weights: Data-Free Model Merging via Subspace Reconstruction

Model merging combines fine-tuned models without retraining, but success ultimately depends on whether the merged model behaves similarly to each expert on its own task. Existing methods largely operate on task-vector weights or their singular subspaces, but without task inputs, they lack effecttive constraints on the merged model's output behavior. We therefore ask how to preserve expert responses without using task inputs during merging? Starting from the observation that matching responses within an expert's output subspace reconstructs its projected output behavior, we construct data-free, layer-local response targets from task vectors and propose Subspace Behavior Reconstruction (SBR). SBR reconstructs each expert's self-response within its task-specific input and output spaces while suppressing unintended cross-task responses. To account for capabilities that may be shared across tasks, we further introduce SBR-R, which preserves cross-task responses on which the two experts agree. Extensive experiments demonstrate that SBR-R more closely preserves expert predictive distributions in CLIP diagnostics and consistently outperforms prior state-of-the-art data-free methods in average performance on CLIP, Qwen-14B LoRA, and InternVL2.5.