Rationality is the quality of being guided by reason, characterized by logical thinking and decision-making that align with evidence and logical rules. This quality is essential for effective problem-solving, as it ensures that solutions are well-founded and systematically derived. Despite the advancements of large language models (LLMs) in generating human-like text with remarkable accuracy, they present biases inherited from the training data, inconsistency across different contexts, and difficulty understanding complex scenarios involving multiple layers of context. Therefore, recent research attempts to leverage the strength of multiple agents working collaboratively with various types of data and tools for enhanced consistency and reliability. To that end, this paper aims to understand whether multi-modal and multi-agent systems are advancing toward rationality by surveying the state-of-the-art works, identifying advancements over single-agent and single-modal systems in terms of rationality, and discussing open problems and future directions. We maintain an open repository at https://github.com/bowen-upenn/MMMA_Rationality.

翻译：理性是指受理性指导的品质，其特征是符合证据与逻辑规则的逻辑思维与决策过程。这一品质对于有效解决问题至关重要，因为它确保解决方案具有充分依据且系统性地推导得出。尽管大语言模型（LLMs）在生成高度拟人化文本方面取得了显著进展，但它们仍存在训练数据带来的偏见、不同语境下的不一致性，以及难以理解涉及多层语境的复杂场景等问题。因此，近期研究尝试利用多智能体协同处理多种数据类型及工具的优势，以提升系统的一致性与可靠性。为此，本文旨在通过综述前沿研究成果，梳理多模态与多智能体系统相较于单智能体单模态系统在理性层面的进展，并探讨现有问题与未来方向，从而理解此类系统是否正在向理性化迈进。我们已在 https://github.com/bowen-upenn/MMMA_Rationality 维护开源资料库。