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.
Language-model agents favor their own group because they have watched their members favor each other. The group label alone does little once the decision has a cost; what drives favoritism is observed...
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...
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: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.
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.
arXiv:2606. 27909v1 Announce Type: cross Abstract: Theory-of-mind evaluations of large language models typically use dyadic social-deduction games, where every observable cue points to a single hidden side, so a model with strong language priors can score well without ever simulating opponents' incentives.
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: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.
arXiv:2608.22152v1 Announce Type: new Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than ac...
arXiv:2606. 30383v1 Announce Type: new Abstract: A rapidly growing class of LLM agents is multi-party: the agent acts for a principal (who briefs it, sends follow-ups, and receives results) while also conversing in a separate channel with a counterparty whose interests may diverge (negotiating with a vendor, screening inbound requests, or mediating between employees).
The study investigates how generative agents in simulated societies revise their stances and reorganise interactions. By combining stance-change metrics, source evaluations, and temporal network analysis across multiple GPT‑4o runs, the authors find that environmental rational persuasion leads to the largest overall stance shift, while economic emotional persuasion produces the highest rate of low‑trust stance changes. In both experimental settings, agents often partially accept new positions yet maintain low trust in the source, and sustained coordination can coexist with ongoing disagreement.
arXiv:2608. 07762v1 Announce Type: new Abstract: LLM benchmarks can build an organization's reputation and attract customers, but only when results are transparent and verifiable.