arXiv:2606. 02423v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their capabilities through extended interactions.
By Ruohao Guo, Wei Xu, Alan Ritter
arXiv:2606. 08172v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited control over how these systems communicate.
By Manuele Reani, Hongjian Zhang, Hongyu Tian
arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.
By Sunnie S. Y. Kim, Margit Bowler, Leon A Gatys
arXiv:2609.13579v1 Announce Type: new
Abstract: Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understandin...
By Fanqi Zeng, Sadid A. Hasan, Chaocheng He
The paper introduces CoCoEval, a framework for evaluating large language model (LLM)–simulated conversations by detecting 10 types of inconsistent and uncollaborative behaviors at the turn level. Using CoCoEval, the authors compare human conversations with those generated by GPT‑4.1, GPT‑5.1, and Claude Opus 4, finding that LLMs produce far fewer such behaviors under vanilla prompting and that prompt engineering or fine‑tuning often over‑produces specific behaviors. The study highlights gaps between human and LLM‑simulated interactions that conventional Likert‑scale evaluations miss, raising concerns about using LLMs as proxies for human social interaction.
By Ryo Kamoi, Ameya Godbole, Binglin Zhou, Xiaoxin Lu, Longqi Yang, Rui Zhang, Mengting Wan, Pei Zhou
arXiv:2607. 19629v1 Announce Type: cross Abstract: Large language models operating in emotionally sensitive contexts face a structural trilemma: when users in vulnerable states request information that may reinforce maladaptive attribution, current response architectures resolve the tension through protective restriction, uninflected facilitation, or unintegrated co-presence of both imperatives -- each preserving one objective at the cost of the other.
By Eunna Lee