analiza aiqbench
Composing insight from the full article — this may take a moment.
Evaluating Context Sensitivity of Collaboration Behavior in Multi Party Simulation Frameworks
arXiv:2611.00089v1 Announce Type: cross Abstract: Group Social Behavior is increasingly used as a lens to study Large language model (LLM) simulations to aid model training and evaluation. However, little prior work has examined how contextual factors such as team size and goal type manifest in long running simulations. To overcome this gap, we conduct a controlled, full-factorial study examining how contextual factors affect collaboration behavior across multi-party simulation frameworks. Using well established social science taxonomies, we quantify context sensitivity of collaboration behavior in simulated teams. Our analyses reveal significant effects of contextual factors such as simulators consistently favor smooth collaboration over disagreement, negotiation pressure, and coordination breakdown. These findings provide the first systematic evidence of how contextual factors influence emergent collaboration in LLM based multi-party simulation frameworks, offering both guidance and empirical results for the use of such simulations.
Citește articolul complet pe arXivSubiecte conexe
Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft
arXiv · 16 iun. 2026
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
arXiv · 4 sept. 2026
Adaptive Collision Sensitivity for Efficient and Safe Human-Robot Collaboration
arXiv · 2 sept. 2026
Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
arXiv · 18 aug. 2026