Anian is a safety‑gated multimodal AI backend designed for perinatal mental‑health support and mindfulness‑intervention routing. It maps user input into a four‑layer hierarchical state representation—emotion, psychosocial constructs, safety risk, and intervention routes—then fuses local and external risk signals to decide whether to generate AI responses or provide fixed safety content. Prototype evaluation on large public corpora showed high classification performance and perfect high‑risk recall in a controlled stress test, though clinical validity remains unestablished.
By Lei Wang, Xiao Wang, Lei Li
The paper evaluates the safety of conversational AI therapy bots for Generation Alpha, revealing that while these models understand 76‑82% of youth‑specific vocabulary, they correctly assess clinical risk only 64‑72% of the time, creating a significant vocabulary‑comprehension gap. Six failure patterns—such as sarcasm masking, minimization acceptance, and semantic drift—were identified, with compounded errors leading to a 94% miss rate when three or more patterns co‑occur. The authors estimate 146,880 missed crises annually and recommend mandatory human‑in‑the‑loop systems, quarterly youth‑specific validation, transparent performance disclosure, and regulatory oversight for youth‑facing mental health AI.
By Manisha Mehta, Virendra Mehta
arXiv:2606. 29685v1 Announce Type: new Abstract: How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm?
By Kaavya Krishna-Kumar, Elaine Lau, Vaughn Robinson, Jay Caldwell, Sheriff Issaka, Skyler Wang, Francisco Guzm\'an, Steven Kelling, Jonas Mueller
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
How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support.
The paper presents a data‑driven study of male domestic violence (MDV) in Bangladesh, using exploratory data analysis to uncover patterns such as verbal abuse prevalence and the influence of financial dependency. It evaluates 10 traditional ML models, 3 deep learning models, and 2 ensemble models, ultimately proposing a stacking ensemble with ANN and CatBoost base classifiers and Logistic Regression meta‑model that achieves 95% accuracy and 99.29% AUC. Explainable AI techniques (SHAP, LIME) and statistical validation confirm the model’s superior performance and highlight key features driving predictions.
By Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Md. Jakir Hossen
arXiv:2609.15855v1 Announce Type: cross
Abstract: % !TEX root = ../main.tex People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk con...
By Laura M. Vowels, Matthew J. Vowels, Shivali Sharma, Apoorv Jha, Rehnuma Choudhury, Wasseem El Sarraj, Rachel Francois-Walcott, Aruba Hussain, Sarah Ingram, Angela Loulopoulou, Adva Segal, Elena Volkova
arXiv:2604. 23786v2 Announce Type: replace Abstract: In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental health.
By Sophie Chiang, Tom Brennan, Fethiye Irmak Dogan, Jiaee Cheong, Hatice Gunes
arXiv:2606. 00087v1 Announce Type: cross Abstract: Effective pre-polysomnography screening for obstructive sleep apnea-hypopnea syndrome (OSAHS) requires combining clinical risk factors with visible craniofacial and neck cues.
By Chen Zhan, Yingchen Wei, Xiaoyu Tan, Jingjing Huang, Xihe Qiu
arXiv:2609.16464v1 Announce Type: cross
Abstract: The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and co...
By Jun Chen, Qi Zhao, Yunliang Jiang, Shuqin Cao, Yunqiang Lin, Chenglong Jia, Qiang Guo, Guang Dai, Xiongtao Zhang, Mengmeng Wang, Xiaoyue Ma
arXiv:2606. 30256v1 Announce Type: new Abstract: Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure.
By Camilo Chac\'on Sartori
The paper presents an AI‑powered, culturally aware chatbot designed to detect stress and provide wellness support for Pakistani university students. Using a Random Forest model trained on 1,100 responses across 20 psychological, physiological, academic, environmental, and social features, the system achieves 89.09% accuracy and a macro F1‑score of 0.89 across three stress severity levels. The model’s outputs are fed to an open‑source large language model via OpenRouter, enabling culturally tailored conversations in English, Urdu, and Roman Urdu, with teacher‑student relationship identified as a key stressor.
By Muhammad Fahad Bashir, Muhammad Afzal