arXiv:2607. 22692v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support despite lacking mechanisms to safely govern evolving mental health risk.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
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.
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
The paper surveys 61 studies on mental‑health AI and identifies a misalignment in how trust is evaluated across disciplines. It proposes a three‑layer framework—human‑oriented, interaction‑oriented, and AI‑oriented trust—and maps stakeholder perspectives onto these layers. The authors argue that future research should focus on calibrating human trust to actual interaction and AI trustworthiness rather than merely maximizing perceived trust.
arXiv:2605. 08827v2 Announce Type: replace Abstract: The safety of mental health AI is often judged at the wrong temporal scale.
arXiv:2606. 26982v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being integrated into mental health support tools and other psychologically sensitive conversational applications.
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...