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This paper introduces SPACE, the first source-free unlearning framework for multimodal large language models (MLLMs), which uses text-guided proxy anchor selection and dual-constraint semantic isolation to erase target concepts without requiring access to original training data, achieving performance comparable to data-dependent methods.
Researchers introduce the MM-OCEAN dataset and a three-tier evaluation framework for grounded personality reasoning in multimodal LLMs, revealing a 'Prejudice Gap' where models often make correct predictions without proper grounding.
SpaceDG is a large-scale dataset and benchmark that evaluates multimodal language models' spatial reasoning robustness under visual degradations like motion blur and low light, revealing significant performance gaps and showing that fine-tuning on SpaceDG improves robustness without degrading clean image performance.