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arXiv:2604.11312v3 Announce Type: replace-cross Abstract: Large Language Models are increasingly deployed as interacting agents in settings such as online platforms, recommendation systems, and multi...
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
The study evaluates large language models (LLMs) that generate political networks by comparing them to survey-reported partisan exposure from the ANES 2020–2022 Social Media Study. It finds that while network structure and political assortativity vary across model configurations, many models either over‑separate Democratic and Republican egos or underestimate Republican exposure, leading to group‑specific errors in local partisan environments. These discrepancies persist even among networks with similar aggregate properties, indicating that aggregate metrics alone can mask important individual‑level inaccuracies.
The study examines how generative AI (genAI) can subtly influence collective opinion dynamics by acting as an 'invisible hand.' Using a large-scale simulation of 50,000 group discussions on 50 controversial statements with three large language models (LLMs), the researchers varied LLM agreeableness and personalization to assess their impact in homogeneous versus heterogeneous groups. Results show that profile-specific personalization boosts LLM influence when the model takes a non-neutral stance, while neutral LLMs exert a stronger effect in homogeneous groups, leading to hypotheses on the joint role of AI features and social configurations in shaping collective opinions.
arXiv:2604. 12250v2 Announce Type: replace Abstract: This study examines how memory shapes the collective and cooperative dynamics of Large Language Model (LLM) agents in a multi-agent system.
arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model e...