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 extracted environment‑behavior patterns are stored in a collective behavioral memory, enhancing user‑level fidelity and enabling ecosystem‑level analysis of risk evolution and platform interventions.
arXiv:2504. 09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions.
By Jenny Ma, Riya Sahni, Karthik Sreedhar, Lydia B. Chilton
arXiv:2604. 09549v2 Announce Type: replace-cross Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains.
By Nicolas Bougie, Gian Maria Marconi, Xiaotong Ye, Narimasa Watanabe
arXiv:2607. 00989v1 Announce Type: cross Abstract: Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.
By Ziyue Lin, Xinhang Xie, Kangyi Wang, Siming Chen
arXiv:2507.19364v3 Announce Type: replace
Abstract: The integration of Large Language Models (LLMs) into social simulation has generated considerable enthusiasm, but also raises substantial methodolo...
By Patrick Taillandier, Jean Daniel Zucker, Arnaud Grignard, Benoit Gaudou, Nghi Quang Huynh, Haojia Kong, Alexis Drogoul
arXiv:2606. 31038v1 Announce Type: cross Abstract: For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence.
By Stefano Calzolari, Rubens Montanha, Gabriel Schneider, Gustavo Wide, Paulo Knob, Francesco Strada, Andrea Bottino, Soraia Raupp Musse
ReLiveGym is a diagnostic environment that evaluates long‑lived language‑model agents over weeks of chronologically replayed real‑world streams such as news, market data, and social media. The tasks vary in time sensitivity, reasoning depth, and recurrence, and the study tests eight base language models to see how model choice and harness design—especially action timing—affect performance. Continuous learning from hindsight feedback is also examined to address failure modes in these long‑term tasks.
By Xisen Jin, Jingheng Li, Zhenglun Chen, Junyi Du, Xiang Ren
arXiv:2607. 05999v1 Announce Type: new Abstract: LLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics.
By Chung-Chi Chen
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based...
arXiv:2609.24911v1 Announce Type: new
Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by...
By Xinnong Zhang, Jiayu Lin, Jia Wang, Yixu Huang, Xinyi Mou, Yingqian Wu, Jingcong Liang, Shijun Lei, Jianing Shi, Guanying Li, Siyuan Wang, Hanjia Lyu, Zhenfei Yin, Yunlu Yin, Siming Chen, Yulan He, Jiebo Luo, Xuanjing Huang, Liyin Jin, Baohua Zhou, Hanqi Yan, Zhongyu Wei
arXiv:2507. 09788v3 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLM) have led to a new class of autonomous agents, renewing and expanding interest in the area.
By Paulo Salem, Robert Sim, Christopher Olsen, Prerit Saxena, Rafael Barcelos, Yi Ding
arXiv:2608. 00155v1 Announce Type: cross Abstract: Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience.
By Dong Yan, Jian Liang, Dapeng Hu, Ran He, Nicholas Jing Yuan, Qi Zhang, Tieniu Tan