AcuLa aligns audio models with medical language models via contrastive and self-supervised objectives on LLM-generated clinical reports, raising mean AUROC from 0.68 to 0.79 across 18 cardio-respiratory tasks.
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FDQ improves stability in multimodal graph unlearning by using feature-dimension aware quantile selection to protect sensitive high-dimensional layers while preserving utility and enabling effective forgetting.
An O-A-R model driven adaptive hierarchical transmission system for multimodal semantic communication achieves over 90% bandwidth savings at 1-3 kbps and eliminates cliff effects in deep fading channels by sending decision-oriented semantic graphs rather than pixels.
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Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding
AcuLa aligns audio models with medical language models via contrastive and self-supervised objectives on LLM-generated clinical reports, raising mean AUROC from 0.68 to 0.79 across 18 cardio-respiratory tasks.
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Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
FDQ improves stability in multimodal graph unlearning by using feature-dimension aware quantile selection to protect sensitive high-dimensional layers while preserving utility and enabling effective forgetting.
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Object-Attribute-Relation Model Driven Adaptive Hierarchical Transmission for Multimodal Semantic Communication
An O-A-R model driven adaptive hierarchical transmission system for multimodal semantic communication achieves over 90% bandwidth savings at 1-3 kbps and eliminates cliff effects in deep fading channels by sending decision-oriented semantic graphs rather than pixels.