MERID is a framework that uses recursive self‑improvement agents to autonomously develop multimodal pipelines for detecting major depressive disorder. It aligns multimodal records with depression targets, jointly modifies representations, fusion, and predictors, and guides revisions through evidence‑guided evolution to validate improvements before inheritance. Experiments on depression benchmarks show MERID outperforms existing multimodal and agent‑based baselines, especially highlighting the importance of acoustic and linguistic cues.
By Lei Liu, Zhaokang Liang, Qingcheng Zeng, Chenda Duan, Lu Mi, Zhen Tan, Tianyu Liu
arXiv:2605.22286v2 Announce Type: replace-cross
Abstract: Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on...
By Zhaomin Wu, Jiayi Li, Bingsheng He
The study investigates how a large language model, Gemma-3-27B-PT, internally represents depressive symptoms. By applying mechanistic interpretability methods to the model’s residual stream, researchers found that symptom groups are geometrically distinct at layer 21, and that projected symptom vectors align with clinician-annotated rankings across mood, somatic, and suicidality dimensions. Additionally, a single depression vector at this layer can differentiate depressive from non-depressive text with an AUC of 0.789, suggesting a potential emotional valence gate for symptom projection.
By Fangyi Zhu, Ajay Subramanian, Allison Constant, Camille Wang, Ravish Gupta, Corey J. Keller
arXiv:2607. 25679v1 Announce Type: cross Abstract: 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.
By Shiyu Teng, Haichen Yu, Jiaqing Liu, Hao Sun, Yu Song, Shurong Chai, Ruibo Hou, Lanfen Lin, Yen-Wei Chen
arXiv:2607. 15202v1 Announce Type: new Abstract: Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research.
By Hoang-Loc Cao, Van Pham, Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Phuc Ho, Veronica Whitford, Hung Cao
arXiv:2607. 22794v1 Announce Type: cross Abstract: Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from inter-speaker variability.
By Ali Tabaraei, Federico Simonetta, Stavros Ntalampiras
The paper examines automatic depression detection from doctor‑patient conversations and finds that models trained on semi‑structured interview data can achieve high accuracy by exploiting fixed interviewer prompts rather than the participants’ language. Across three datasets (ANDROIDS, DAIC‑WOZ, E‑DAIC), the authors show that restricting models to participant utterances distributes decision evidence more broadly and reflects genuine linguistic cues. The study highlights a cross‑dataset, architecture‑agnostic bias introduced by interviewer prompts and calls for analyses that localize decision evidence by time and speaker to ensure models learn from participants’ language.
By Hasindri Watawana, Sergio Burdisso, Diego A. Moreno-Galv\'an, Fernando S\'anchez-Vega, A. Pastor L\'opez-Monroy, Petr Motlicek, Esa\'u Villatoro-Tello
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability.
arXiv:2606. 10796v1 Announce Type: cross Abstract: Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems.
By Yiqing Lyu, Xianbing Zhao, Buzhou Tang, Ronghuan Jiang
arXiv:2608.21868v1 Announce Type: new
Abstract: Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile....
By Ao Chen, Xiaojiang Peng
arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.
By Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal
The paper introduces SMILE, a self‑explainable multimodal information bottleneck framework for medical diagnosis. It jointly optimizes predictive accuracy and modality‑specific explainability by selecting the most informative elements within each data modality. Experiments on diverse medical datasets show strong diagnostic performance, including a 9.1‑percentage‑point accuracy gain on the iCTCF dataset, and provide transparent, modality‑aware explanations that enhance both explainability and generalization.
By Yuqing Yang, Alexander Schmatz, Zhaozhao Ma, Changkyu Choi, Robert Jenssen, Shujian Yu