MERID：用于重度抑郁症分析的多模态递归自我改进智能体探索

中文摘要 AI 辅助

重度抑郁症（MDD）严重影响日常活动和生活质量。检测MDD涉及多模态数据，如访谈录音和传感器测量。这尤其具有挑战性，因为这些异质模态通常需要不同的定制预测流程。为应对这一挑战，现有工作已探索了手动设计的多模态架构和智能体辅助的流程开发。尽管取得了进展，但根据实验反馈自主修订流程并将验证过的改进带入后续设计仍然困难。为此，我们提出了用于重度抑郁症分析的多模态递归自我改进智能体探索（MERID）。该框架通过基于经验的递归自我改进（RSI）开发抑郁症流程。接地状态构建（GSC）通过将多模态记录与受试者级别的抑郁症目标对齐来接地经验。耦合流程探索（CPE）联合修改表示、融合和预测器，以构建用于分类和严重程度估计的后继流程。证据引导进化（EGE）通过反馈指导修订，并在小型抑郁症队列中的不确定性下验证收益后再继承。在抑郁症基准上的广泛实验表明，与多模态和智能体基线相比，MERID在多个任务上取得了最佳结果。进一步分析强调了声学和语言线索对抑郁症检测的价值。我们的代码可在以下网址获取：https URL

英文摘要

Major depressive disorder (MDD) severely impacts daily activities and quality of life. Detecting MDD involves multimodal data, such as interview recordings and sensor measurements. This is particularly challenging, as these heterogeneous modalities often demand distinct, customized prediction pipelines. Existing efforts to address this challenge have explored both manually engineered multimodal architectures and agent-assisted pipeline development. Despite their progress, it remains challenging to autonomously revise pipelines based on experimental feedback and carry verified improvements forward into subsequent designs. To this end, we propose Multimodal Exploration via Recursive Self-Improvement Agents for Major Depression Analysis (MERID). The framework develops depression pipelines through experience-based recursive self-improvement (RSI). Grounded State Construction (GSC) grounds experience by aligning multimodal records with subject-level depression targets. Coupled Pipeline Exploration (CPE) jointly modifies representations, fusion, and predictors to build successor pipelines for classification and severity estimation. Evidence-Guided Evolution (EGE) guides revisions through feedback and verifies gains under uncertainty in small depression cohorts before inheritance. Extensive experiments on depression benchmarks show that MERID achieves the best results on multiple tasks compared with multimodal and agent-based baselines. Further analysis highlights the value of acoustic and linguistic cues for depression detection. Our code is available at https://github.com/DiscoAILab/MERID

发表机构

• Yale University（耶鲁大学）
• Zhejiang University（浙江大学）
• Northwestern University（西北大学）
• University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
• Tsinghua University（清华大学）
• Stevens Institute of Technology（史蒂文斯理工学院）

机构由 AI 辅助整理，请以论文原文为准。