新框架DynaBridge用多模态数据和语义摘要预测抑郁焦虑压力，在AdoDAS上F1达0.5，比基线强。
DynaBridge是一种动态摘要引导的跨任务多模态融合框架，用于基于DASS-21结构的抑郁、焦虑和压力评估。该模型编码多个会话中的声学、视觉和文本线索，并利用冻结LLM生成DASS感知的参与者级语义摘要。它预测有序项目分布，从项目级软评分重建抑郁/焦虑/压力风险证据，并与直接多模态风险预测融合。在AdoDAS验证集上，DynaBridge实现了0.5012的平均F1分数（D/A/S风险预测）和0.3216的平均QWK（DASS-21项目预测），优于官方基线和其他代表性多模态方法。
DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment
Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels. In DASS-21, risk labels are derived from ordered symptom items through fixed item-to-subscale mappings. We propose \textbf{DynaBridge}, a dynamic summary-guided cross-task multimodal framework for DASS-structured mental health assessment. DynaBridge encodes acoustic, visual, and textual cues across multiple sessions and augments them with frozen-LLM-generated DASS-aware summaries as participant-level semantic evidence. It predicts ordinal item distributions, reconstructs depression, anxiety, and stress risk evidence from item-level soft scores, and fuses this evidence with direct multimodal risk predictions. A confidence-aware refinement strategy further incorporates high-confidence semantic cues conservatively. On the official AdoDAS validation split, DynaBridge outperforms the official baseline and representative multimodal methods, achieving 0.5012 mean F1 for D/A/S risk prediction and 0.3216 mean QWK for DASS-21 item prediction. These results show the value of bridging multimodal cues, semantic summaries, and DASS-21 psychometric structure.