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Semantic relevance is a dangerously loose proxy for safety: replacing standard RAG with formal, scene-grounded rule entailment prevents vision-language planners from acting on ungrounded risk assumptions.
The developed BrainVLM model, which integrates an uncertainty quantification strategy to indicate prediction reliability and a module for generating radiology reports to elucidate the clinical rationale, was trained on multi-modal data from 40,043 individuals.
Qualitative and quantitative experiments demonstrate that FRPSS achieves the best Single Image Fr\'echet Inception Distance (SIFID) scores on all three datasets while maintaining competitive Learned Perceptual Image Patch Similarity (LPIPS).
RSFusionDet not only outperforms existing models in underwater object detection but also achieves reliable cross-modal object matching, setting a new benchmark in the field.
