@conference {2025,
	title = {Measuring and Mitigating Rapport Bias of Large Language Models under Multi-Agent Social Interactions},
	booktitle = {Proceedings of ICLR},
	year = {2026},
	address = {Rio de Janeiro, Brazil},
	abstract = {Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS) as components of collaborative intelligence, where peer interactions dynamically shape individual decision-making. Although prior work has focused on conformity bias, we extend the analysis to examine how LLMs form trust from previous impressions, resist misinformation, and integrate peer input during interaction, key factors for achieving collective intelligence under complex social dynamics. We present KAIROS, a benchmark simulating quiz contests with peer agents of varying reliability, offering fine-grained control over conditions such as expert-novice roles, noisy crowds, and adversarial peers. LLMs receive both historical interactions and current peer responses, allowing systematic investigation into how trust, peer action, and self-confidence influence decisions. As for mitigation strategies, we evaluate prompting, supervised fine-tuning, and reinforcement learning, Group Relative Policy Optimisation (GRPO), across multiple models. Our results reveal that GRPO with multi-agent context combined with outcome-based rewards and unconstrained reasoning achieves the best overall performance, but also decreases the robustness to social influence compared to Base models. The code and datasets are available at: https://github.com/declare-lab/KAIROS},
	url = {https://arxiv.org/abs/2508.18321},
	author = {M. Song and T. D. Pala and W. Jin and A. Zadeh and C. Li and D. Herremans and S. Poria}
}
@article {2025,
	title = {Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics},
	journal = {arXiv:2510.05137},
	year = {2025},
	url = {https://arxiv.org/abs/2510.05137},
	author = {M. Song and R. Liu and Wang, Xinyu and Jiang, Yong and Xie, Pengjun and Huang, Fei and Zhou, Jingren and D. Herremans and S. Poria}
}
