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This work establishes a skill anti-distillation paradigm for protecting personal privacy and labor autonomy against unauthorized skill distillation, and introduces CounterPersona, which constructs targeted counter-persona evidence, packs compatible behavioral states into compact realization units, and strengthens them through rationale-guided consistency rewriting.
RAPID is proposed, a self-referenced latent maximization framework that removes external dependencies and encourages the same model update to induce consistently disruptive effects across training samples, thereby improving reachability and establishing a paradigm for real-time protection against unauthorized distillation in deployed T2I systems.
Achieving up to a 145x speedup in fuzzy PSI protocols could redefine the efficiency benchmarks for secure multi-party computations.
Malicious modifications to router weights can turn Mixture-of-Experts models into trigger-controlled bottlenecks, revealing a critical vulnerability in AI serving architectures.
