Foundation models pretrained on similar data distributions tend to learn overlapping semantic structure, but their representations are distinct due to different model architecture, pretraining objectives and protocols. This prevents unified interpretability and cross-model feature reuse. Existing approaches extract interpretable concepts from individual models and align these concepts in a shared coordinate system using cross-reconstruction objectives, but the alignment only applies to latent coordinates while the concepts themselves remain model-specific and cannot be shared or reused. This paper introduces the Gated Bottleneck Sparse Autoencoder (GB-SAE), which casts unified cross-model interpretability as a representation factorization problem and solves it by simultaneously learning a unified shared dictionary and multiple private model-specific dictionaries via a gated bottleneck sparse autoencoder framework. The shared dictionary encodes the cross-model common semantic structure in unified concepts that are reusable across all participating models (and beyond), while each private dictionary comprises residual concepts that capture model-specific biases. We evaluate GB-SAE with distinct foundation models including ViT, SigLIP, DINOv2, and ConvNeXt. GB-SAE demonstrates superior reconstruction capability, unified and faithful interpretability, strong reusability and generalization to unseen models and downstream tasks. The code is available at https://github.com/trustAIoT/GB-SAE_NeurIPS.

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