Hugging Face Trending Papers
Why LLM Agents Favor Their Group: Stakes, Observed Norms, and Reputation
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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.
arXiv AI
Sep 30
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...
arXiv Computation and Language
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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.
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.
arXiv AI
Aug 5
Language model agents show in-group trust bias invisible to standard behavioural audits
arXiv:2605. 28114v2 Announce Type: replace Abstract: Language-model agents are moving from single-user assistants into persistent networks that build trust and reputation with one another, and the same models increasingly control physically embodied robots as well as software.
arXiv AI
1d ago
Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma
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.