arXiv:2610.16231v1 Announce Type: new
Abstract: As large language models are increasingly adopted as multi-party communication facilitators, there is a growing demand for agents capable of balancing...
By Ziyi Liu, Tzu-Sheng Kuo, Jiale Liu, Deqing Fu, Nithum Thain, Crystal Qian
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:2608.30756v1 Announce Type: cross
Abstract: Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their...
By Annemarie Wittig, Alina Mailach, Janet Siegmund, Norbert Siegmund
arXiv:2610.09569v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world...
By Shivam Shukla, Jihye Kim, Shubham Gaur, Mahnaz Roshanaei, Magy Seif El-Nasr
The paper introduces CAPA, a Collaborative Agent Predictive Architecture designed to give large language model (LLM) agents situational awareness in online meetings. CAPA uses a Perceiver to update meeting state, a Predictor to forecast conversation flow, a Controller to decide speaking actions, and a Generator to phrase contributions. Evaluated on 137 AMI meetings, CAPA reduces the silence rate from 51.4% to 2.5%, doubles credited recovery, and maintains low hallucination, demonstrating that structured state tracking is key to effective delegation.
By Muneeb Khan, Frederic Kirstein, Terry Ruas, Bela Gipp
Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world relationships. Yet existing evaluations primarily...