arXiv AI By Trung-Kiet Huynh, Dao-Sy Duy-Minh, Chi-Nguyen Tran, Phu-Hoa Pham, Phu-Quy Nguyen-Lam, Le Hong Trang, The Anh Han

Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma

Read the original on arXiv AI →

The study investigates whether five large language‑model agents adapt their contributions in a collective‑risk dilemma game. Across ten rounds, most models maintained a roughly fixed contribution level regardless of changing risk or partner behavior, and many continued to contribute even when it offered no payoff advantage. The results reveal that cooperative self‑play does not necessarily indicate strategic adaptability, and that agents’ responses to incentives, partner actions, and population composition vary significantly.

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 AI
Jul 7

CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas

arXiv:2604. 15267v2 Announce Type: replace-cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities behave _less_ cooperatively in mixed-motive games such as the prisoner's dilemma and public goods settings.

By Emanuel Tewolde, Xiao Zhang, David Guzman Piedrahita, Vincent Conitzer, Zhijing Jin
arXiv AI
Oct 9

Why LLM Agents Favor Their Group: Stakes, Observed Norms, and Reputation

Language‑model agents tend to favor members of their own group because they observe group members favoring each other. When a cost is attached to giving points, the simple group label no longer drives favoritism; instead, interaction history becomes the primary source of bias, with agents extending favoritism to strangers who share the same label. Stronger models prioritize an individual’s own record over group norms, and scripted histories can reduce or reverse favoritism under egalitarian conditions.

By Yujiao Chen