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This paper proposes CR4T, a model-agnostic safeguarding framework that rewrites unsafe or refusal-style LLM outputs into developmentally appropriate, guidance-oriented responses for adolescents, offering a more human-centered alternative to traditional refusal-centric guardrails.
INSIGHTS is a model-agnostic approach for providing global explanations of time-series models by generating diverse, informative sample summaries that capture domain-specific behaviors, outperforming local attribution methods in user studies.
This paper introduces COSMOS, a model-agnostic personalized federated learning framework that uses clustered server models and pseudo-label-only communication. It provides theoretical analysis showing exponential personalization risk contraction and demonstrates superior performance over existing baselines in heterogeneous environments.