Defines Decision Potential Surface (DPS) whose zero isohypse equals an LLM decision boundary and supplies a K-sample approximation algorithm with derived upper bounds on absolute, expected, and concentration errors.
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A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
Quantization of neural classifiers produces measurable boundary shifts captured by Jaccard distances and flip rates that correlate between calibration and held-out sets across bit widths.
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Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
Defines Decision Potential Surface (DPS) whose zero isohypse equals an LLM decision boundary and supplies a K-sample approximation algorithm with derived upper bounds on absolute, expected, and concentration errors.
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Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifiers
Quantization of neural classifiers produces measurable boundary shifts captured by Jaccard distances and flip rates that correlate between calibration and held-out sets across bit widths.