PERSONAWEAVER is a new approach to procedural character generation that separates world building from behavioral specification, using manually curated banks of moral positions and conversational reactions to diversify character behavior. By applying this method across ten realistic and fantastical settings and three large language models, the system produces broader moral and interactional response distributions, varied interpersonal language, response length, sentiment, and less archetypal world attribute combinations compared to prior work.
By Maan Qraitem, Kate Saenko, Bryan A. Plummer
arXiv:2607. 09766v1 Announce Type: new Abstract: AI agents are increasingly deployed in shared environments where they pursue diverse goals and compete for rewards.
By Yaowen Ye, Jacob Steinhardt
arXiv:2605.12894v2 Announce Type: replace-cross
Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with diverse users, including those who are uncle...
By Harshita Chopra, Kshitish Ghate, Aylin Caliskan, Tadayoshi Kohno, Chirag Shah, Natasha Jaques
LiveSim is an LLM-based framework designed to simulate users in multi‑agent live‑stream ecosystems. It treats users as editable behavioral hypotheses and refines them through trajectory‑grounded interactions, using discrepancies between simulated and observed trajectories to uncover missing environmental shaping effects. The framework extracts transferable environment‑behavior patterns, stores them in a collective behavioral memory, and demonstrates improved user‑level fidelity and ecosystem‑level analysis on real‑world live‑stream risk‑control data.
By Jiaqi Xu, Yiran Qiao, Jing Chen, Qiwei Zhong, Xiang Ao, Xueqi Cheng
arXiv:2609.01491v1 Announce Type: cross
Abstract: The growing rate at which LLM agents interact with one another raises key questions about language evolution in multi-LLM-agent settings, with implic...
By Elias Stengel-Eskin, Newton Sander, Carlos Bonetti, Sasha Boguraev, James Bowler, Hale Sirin, Simon Kirby
arXiv:2606. 29113v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of communication and deception.
By Quanyan Zhu