Existing model merging techniques can be broadly categorised into weight-based, subspacebased, and routing-based approaches. Advanced weight-based methods typically utilise optimisation algorithms or geometric interpolations to determine optimal task vector coefficients while preserving the capabilities of independent models. Subspace-based methods involve pruning or masking to eliminate insignificant parameters before merging sparse models in the parameter subspace, thereby reducing task interference. By contrast, routingbased methods adaptively combine models during inference, adjusting the merge dynamically per the specific input . We experiment with weight-and subspace-based merging in a crossmodal context.