Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
Vikrant Varma, Rohin Shah, Zachary Kenton, J ´anos Kram ´ar, and Ramana Kumar
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper identifies a concept-layer topological alignment bottleneck in text-to-video diffusion models and introduces the CLEAR separability-driven optimization framework for targeted concept erasure.
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Grokking of Diffusion Models: Case Study on Modular Addition
Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
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Where Concept Erasure Should Occur: Concept-Layer Alignment in Text-to-Video Diffusion Models
The paper identifies a concept-layer topological alignment bottleneck in text-to-video diffusion models and introduces the CLEAR separability-driven optimization framework for targeted concept erasure.