Beyond Interpretability: When, Why, and How Sparse Autoencoders Enable Label-Free Visual Steering
arXiv:2506.01247v3 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) are increasingly used to interpret foundation models, but their role as an actionable intervention space remains less understood, especially in vision. We study whether sparse visual features can be used not only for post-hoc analysis, but also to steer frozen vision-language models. We introduce Visual Sparse Steering (VS2), a label-free method that trains a top-$k$ SAE on unlabeled activations from a frozen CLIP image encoder and, at test time, constructs an interpretable steering vector by amplifying the in
Ongoing research into foundation model interpretability and steerability is rapidly generating new techniques as AI capabilities advance.
The ability to visually steer foundation models without explicit labels represents a significant step towards more controlled and adaptable AI systems, improving safety and utility.
AI models could become more directly controllable and debuggable through interpretable features, moving beyond post-hoc analysis to active intervention.
- · AI developers
- · Foundation model users
- · Researchers in explainable AI
Improved fine-grained control and interpretability for vision-language models without needing extensive labeled datasets.
Accelerated development of more robust and trustworthy AI applications in visual domains.
New interfaces and methodologies for human-AI interaction could emerge based on direct feature steering.
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