Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers
arXiv:2606.06664v1 Announce Type: cross Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment. Sparse autoencoders (SAEs) provide a promising lens for decomposing model representations into human-interpretable concepts, yet adapting SAE-based interpretation to ViTs remains challenging due to limited control over concept coverage and subjective, non-scalable feature interpretation. To fill the gaps, motivated by neuroscience-inspired principles, we propose ViSAE, a mechanis
The increasing sophistication and deployment of Vision Transformers necessitate more robust interpretability methods to ensure safe and reliable AI systems, especially as 'black box' issues become critical in real-world applications.
Improved interpretability of ViTs addresses a core challenge in AI development—understanding and steering complex models, which is crucial for their adoption in high-stakes environments and for building trust in AI.
This research provides a more scalable and interpretable approach to dissecting ViT behavior, moving beyond subjective analyses and offering a pathway to mitigate biases and identify spurious correlations in visual AI models.
- · AI Safety Researchers
- · AI Developers
- · High-stakes AI Industries
- · Ethical AI Initiatives
- · Developers of Undifferentiated 'Black Box' AI
- · Companies with Poor AI Governance
- · Inadequate AI Interpretability Methods
Wider adoption of Vision Transformers in sensitive applications due to enhanced interpretability and control capabilities.
Reduced incidence of unforeseen failures or biased outcomes in AI systems, leading to higher public and regulatory trust.
Potential for new regulatory frameworks and industry standards mandating specific levels of AI interpretability for deployment.
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Read at arXiv cs.LG