# DepressionAgent: Reading, Listening, Seeing, and Deliberating Multimodal Evidence for Depression Risk Assessment

arXiv: https://arxiv.org/abs/2608.13891 · HTML: https://mlsanity.com/paper/2608.13891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2026.

## Built on by (most cited first, 1 of 1)

- [MERID: Multimodal Exploration via Recursive Self-Improvement Agents for Major Depression Analysis](https://mlsanity.com/paper/2609.36235/) (2026)

## Abstract (arXiv)

Multimodal depression risk assessment requires jointly interpreting textual, acoustic, and visual cues that are often subtle, non-specific, context-dependent, and potentially inconsistent across modalities. Existing multimodal approaches predominantly learn latent representations through feature fusion, leaving the evidence underlying a prediction and the treatment of cross-modal disagreement largely implicit. We propose DepressionAgent, an evidence-centric agentic framework that transforms multimodal depression assessment from implicit feature fusion into explicit evidence deliberation. DepressionAgent first converts textual, acoustic, and visual inputs into modality-specific evidence, and then organizes self-report and behavioral evidence into parallel support--challenge deliberation branches. Cross-modal arbitration explicitly examines agreement and disagreement between the two branches, with conflict reflection revisiting inconsistent assessments before decision making. A subsequent risk reflection mechanism provides an independent textual second opinion for initially low-risk cases to reduce potentially missed risk signals. Without depression-specific supervised training or parameter fine-tuning, DepressionAgent achieves competitive performance on multiple public benchmarks. Extensive ablations, cross-model evaluations, qualitative analyses, and clinician assessments further demonstrate the effectiveness and inspectability of the proposed framework.