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This paper studies temporal knowledge graph forecasting under controlled distribution shifts using a synthetic generator that encodes recurrence, homophily, and periodicity. Experiments on seven architectures reveal signal-dependent robustness and limitations in model adaptivity to structural breaks.
SAE-FT introduces a novel fine-tuning method for CLIP models that uses sparse autoencoder constraints to regularize visual representations, improving robustness against distribution shifts while maintaining performance and enabling interpretability.