AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes EraseSAE, a novel framework that leverages sparse autoencoders to achieve surgical concept erasure in DiT-based T2V diffusion models via a principled decompose-attribute-erase pipeline, and introduces the Partitioned Convolutional Sparse Autoencoder.
A unified geometry-aware memory framework is proposed, whose patch-memory and tiled-PRoPE components introduce no additional learnable parameters, combining explicit 3D patch retrieval with projective camera conditioning to enable geometry-consistent camera control and faithful long-horizon scene recall.
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