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This paper introduces Bag-of-Words Superposition (BOWS), a framework for studying superposition in neural networks with realistic feature correlations, demonstrating that interference can be constructive rather than merely noise. The authors argue that traditional views of superposition fail to account for the structured representations observed in real language models, where correlated features can enhance signal reconstruction. Key contributions include the formalization of linear superposition and the ob

Author

tinysticks5

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EN