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Beyond the Linear Representation Hypothesis: Non-Linear Activation Steering in Text-to-Image Models
Author1, Author2, Author3, Author4, Author5
multimodalinterpretabilityneural networks
2610.06945
Builder Relevance
1h ago70%
Abstract
This paper challenges the Linear Representation Hypothesis by demonstrating that text-to-image models represent intermediate visual states nonlinearly.
Reality Card
Core Claim
KANSteer provides a more accurate and interpretable method for traversing concept representations in text-to-image models compared to linear steering.
Method / Result
KANSteer shows that activation trajectories deviate from straight lines, offering smoother traversals of intermediate attributes.
Limitations
The generalizability of KANSteer across all types of visual concepts remains untested.
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