arXiv:2607.08605v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have emerged as a promising technique for mechanistic interpretability by learning a set of sparse latent features in large models, each of which encodes a distinct concept. However, in vision-language models (VLMs), vanilla SAEs struggle to learn modality-consistent concepts, with concepts often exhibiting fragmented coverage (i.e., disjoint regions) in the visual modality. To address this challenge, we propose a Structured Sparse AutoEncoder ($S^2AE$) that enforces concept consistency from both semantic and spatial
The proliferation of advanced AI models, specifically large vision-language models, necessitates new methods for mechanistic interpretability to understand their internal workings and ensure robust generalization.
Improved interpretability of multisensory AI systems like VLMs is critical for debugging, safety, and developing more generalized and reliable AI, especially in complex applications.
The ability to learn consistent, interpretable concepts across different data modalities (e.g., vision and language) can lead to more robust and explainable AI models, accelerating their adoption in sensitive domains.
- · AI researchers and developers
- · AI safety organizations
- · Companies deploying multimodal AI
- · Vision-language model users
- · Developers of 'black box' AI models
- · Early, less interpretable multimodal AI systems
Enhancement of AI interpretability allows for better debugging and understanding of large models.
More interpretable and reliable multimodal AI systems could accelerate adoption in critical sectors like healthcare, autonomous vehicles, and defence.
A deeper understanding of AI's internal representations may inform the development of more human-like cognitive architectures and general AI.
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Read at arXiv cs.LG