arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.
By Andrea Cera Palatsi, Samuel Martin-Gutierrez, Ana S. Cardenal, Max Pellert
arXiv:2610.03033v1 Announce Type: new
Abstract: Large language model (LLM)-based agents increasingly operate in multi-agent systems (MAS) characterised by strategic interaction. However, little is kn...
By Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano, The Anh Han, German Castignani, Pietro Li\`o
Fig (Finding Interactions in Games) is an agentic system that performs targeted elicitation interventions on game environments to study multiagent behaviors. It takes a natural‑language behavior description, decomposes it into sub‑behaviors, generates or selects appropriate social games, and applies interventions to the game engine, opponents, instructions, and context. The system evaluates model behavior using mechanical checks and synthetic rubric judgments, and human review validates the interventions, generated games, and evaluation mechanisms. In experiments, models attempted the target behavior in 45.2% of episodes on average for six test behaviors, and Fig’s interventions increased attempt rates by 10.9% compared to matched control games.
By Anrui Gu, Zhichao Wang, Nicolai Ouporov
The paper introduces SILICA, an open instrument designed to evaluate whether large language model (LLM) agent societies replicate human behavioural distributions. Using five environments with human‑anchored data and perturbations, the study finds that most LLMs only match human behaviour at initial stages, failing to reproduce end‑state cooperation or correct acceptance thresholds. The results suggest that current LLM societies can support exploratory claims but do not yet reliably emulate human social dynamics.
By Raad Bin Tareaf
arXiv:2607. 17948v1 Announce Type: new Abstract: Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents.
By Stefano Blando, Emanuele Guerrazzi, Riccardo Porcedda, Giuseppe Squillace, Max Tschaikowski, Andrea Vandin
arXiv:2610.15788v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly deployed as teams, yet they fail in characteristically social ways: information holders stay silent,...
By Yirui Qi, Xiaoming Zhang, Jaehong Yoon