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This paper introduces Orthogonal Concept Erasure (OCE), a method for precisely removing target concepts from diffusion models using multiplicative orthogonal parameter updates, enabling efficient single- and multi-concept erasure up to 100 concepts in seconds.
InfoQuant introduces a train-free method, Peak Suppression Orthogonal Transformation (PSOT), to reshape activation distributions for low-bit LLM quantization, preserving 97% floating-point accuracy under W4A4KV4 and outperforming prior PTQ methods.
This paper introduces Pion, a novel spectrum-preserving optimizer for large language model training that uses orthogonal equivalence transformations to maintain singular values during weight updates, offering stable performance comparable to standard optimizers.