arXiv AI June 30, 2026 By Henrique Ferraz de Arruda, Carlos Gracia L\'azaro, Alberto Aleta, Yamir Moreno

Collective cooperation without individual fidelity in LLM agents

arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Aug 31

Benchmarking large language model agent societies against human behavioural distributions

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

llms agents benchmarks

arXiv AI

Aug 13

Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation

arXiv:2608. 12125v1 Announce Type: cross Abstract: As LLM-based agents with user-instructed goals are becoming widely deployed, they increasingly encounter each other in strategic interactions, and face challenges of finding mutually beneficial outcomes.

By Akash Kundu, Emanuel Tewolde, Ratip Emin Berker, Samuel F. Brown, Vincent Conitzer

llms agents

arXiv AI

Jul 2

Large language models replicate and predict human cooperation across experiments in game theory

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

llms agents safety

arXiv AI

4d ago

Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems

arXiv:2609.35928v1 Announce Type: cross Abstract: Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantl...

By Xavier Del Giudice, Alessio Palma, Matteo Migliarini, Fabio Galasso, Indro Spinelli

llms agents benchmarks

arXiv AI

2d ago

High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

arXiv:2604.02578v2 Announce Type: replace-cross Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question...

By Sahaj Singh Maini, Robert L. Goldstone, Zoran Tiganj

llms safety

arXiv AI

Jun 9

Payoff scaling shapes cooperation in LLM agents across languages

arXiv:2601. 19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users.

By Trung-Kiet Huynh, Dao-Sy Duy-Minh, Thanh-Bang Cao, Phong-Hao Le, Hong-Dan Nguyen, Phu-Quy Nguyen-Lam, Minh-Luan Nguyen-Vo, Hong-Phat Pham, Phu-Hoa Pham, Thien-Kim Than, Chi-Nguyen Tran, Huy Tran, Gia-Thoai Tran-Le, Alessio Buscemi, Le Hong Trang, The Anh Han

llms agents safety

One email a morning, machine-written

Related stories

Benchmarking large language model agent societies against human behavioural distributions

Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation

Large language models replicate and predict human cooperation across experiments in game theory

Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems

High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

Payoff scaling shapes cooperation in LLM agents across languages