RESEARCH · RESEARCH · #1235
MERID — Multimodal Recursive Self-Improvement Agents for Major Depression Analysis (arXiv:2609.36235v1)
This arXiv preprint introduces MERID, a framework that uses recursive self-improvement agents to iteratively develop multimodal pipelines for detecting and estimating severity of major depressive disorder. MERID combines Grounded State Construction (GSC), Coupled Pipeline Exploration (CPE), and Evidence-Guided Evolution (EGE), reports improved performance versus multimodal and agent-based baselines on depression benchmarks, and publishes code at GitHub: DiscoAILab/MERID.
KEY POINTS
- This arXiv preprint introduces MERID, a framework that uses recursive self-improvement agents to iteratively develop multimodal pipelines for detecting and estimating severity of major depressive disorder.
- MERID combines Grounded State Construction (GSC), Coupled Pipeline Exploration (CPE), and Evidence-Guided Evolution (EGE), reports improved performance versus multimodal and agent-based baselines on depression benchmarks, and publishes code at GitHub: DiscoAILab/MERID.
- Automating and verifying iterative pipeline revisions for multimodal MDD detection could speed development of more effective clinical ML tools and highlight useful acoustic/linguistic signals in small cohorts.
WHY IT MATTERS
Automating and verifying iterative pipeline revisions for multimodal MDD detection could speed development of more effective clinical ML tools and highlight useful acoustic/linguistic signals in small cohorts.