Framework improves multimodal depression detection with self-updating pipelines
MERID: Multimodal Exploration via Recursive Self-Improvement Agents for Major Depression Analysis
Artificial IntelligenceMachine LearningMultiagent Systems
Summary
Detecting major depression is hard because it uses many types of information, like speech and sensors, that need different ways to analyze them. The authors introduce MERID, a system that learns from its own past attempts to improve how it detects and measures depression. It automatically updates its analysis methods using feedback and checks improvements carefully before keeping them. Their tests show MERID works better than other methods on several depression detection tasks. They also found that sounds and language in speech are especially helpful for spotting depression.
What this means in practice
- •For mental health software developers: Build automated tools that improve depression diagnosis by updating multimodal data analysis pipelines with feedback.
- •For speech technology engineers: Enhance systems analyzing speech patterns for mental health by incorporating joint exploration of sound and language features.
Authors
Lei Liu, Zhaokang Liang, Qingcheng Zeng, Chenda Duan, Lu Mi, Zhen Tan, Tianyu Liu
Abstract
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