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To improve attack effectiveness, the farthest-label targeted attack strategy is proposed, which selects adversarial vectors that deviate most from the model's coarse-grained labels, thereby maximizing disruption.
A new multimodal ICL framework is proposed that combines contrastive demonstration modeling with the self-refinement capability of MLLMs and consistently improves MLLM performance, with particularly notable gains on visual question answering (VQA).
This work introduces a Variational Semantic Prompt Extractor (VSPE), which adaptively aggregates anomaly-relevant local semantics from dense patch tokens and regularizes them through a variational information bottleneck, thereby incorporating fine-grained visual cues and enabling more precise cross-modal alignment.
Performance-Driven Demonstration Selection (PDDS), which directly aligns demonstration selection with ICL performance, is proposed, which formulates selection as predicting the target LLM’s downstream task performance for a given query–in-context pair, replacing proxy heuristics with a performance-aware objec-tive.
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