pQLL calculi assign real-valued strength to proofs, generalize hypersequent and deep inference systems, prove cut elimination, and achieve completeness for soft residuated lattices, recovering MALL as p goes to infinity.
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Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
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Quantitative Linear Logic
pQLL calculi assign real-valued strength to proofs, generalize hypersequent and deep inference systems, prove cut elimination, and achieve completeness for soft residuated lattices, recovering MALL as p goes to infinity.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.