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The paper introduces GoldiMask, a method for supervised fine-tuning of diffusion language models that selects context tokens via submodular optimization and weights prediction targets based on context benefit, achieving improved accuracy on reasoning and code generation benchmarks across multiple backbones.
This paper studies adversarial attacks on continuous data summarization under similarity-level perturbations via DR-submodular optimization, proposing multi-target attack generation as a min-max problem and robust defense as a regularized max-min problem, with theoretical guarantees and experiments.