arXiv:2606. 09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through interfaces-that supports usability, trust, and safety.
By James Pierce, Vaiva Kalnikait\.e, Siddharth Gupta, Brian Granger
arXiv:2608. 15304v1 Announce Type: new Abstract: Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition.
By Guanchu Wang, Qinuo Li, Mengnan Du, Xia Hu, Bowen Zhou
arXiv:2607. 02245v1 Announce Type: new Abstract: Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma.
By Seren Yenikent, Jack Vinijtrongjit, Katherine Ng
arXiv:2509.08494v2 Announce Type: replace-cross
Abstract: As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures....
By Benjamin Sturgeon, Daniel Samuelson, Jacob Haimes, Jacy Reese Anthis
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.
By Xin Sun, Yue Su, Yifan Mo, Qingyu Meng, Yuxuan Li, Min Chen, Mengyuan Zhang, Saku Sugawara, Charlotte Gerritsen, Sander L. Koole, Koen Hindriks, Jiahuan Pei
The paper introduces Synthetic Linguistic Agency (SLA), a framework that defines linguistic agency in terms of embodiment, participation, and precariousness. It presents two studies: one that operationalizes SLA criteria and identifies existing systems, and another that builds an Embodied Mortal Agent (EMA) using mortality‑grounded reinforcement learning. Experiments show the EMA’s linguistic choices depend on its body and social history, influence partner behavior, and adapt over time, demonstrating SLA in an artificial agent.
By Sixin Chen, Taizhou Chen