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Decayfmt is a Rust-based file format that intentionally corrupts data each time it is opened, simulating digital decay for artistic or conceptual purposes.
This paper introduces K-Inverse-RFM, a modified Recursive Feature Machine that bridges the performance gap between RFMs and neural networks on mathematical tasks corrupted by label noise, imbalanced data, or alternative data representations.
A 1.7B parameter model surpasses 744B GLM-5 on Schema Guided Dialogue despite corrupted training data, showing 437× size efficiency.