Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey
<|reference_start|>Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey: 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.<|reference_end|>
@article{jiang2024towards,
    title={Towards Rationality in Language and Multimodal Agents: A Survey},
    author={Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Yuan Yuan, Zhuoqun Hao,
  Xinyi Bai, Weijie J. Su, Camillo J. Taylor, Tanwi Mallick},
    journal={arXiv preprint arXiv:2406.00252},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00252},
    primaryClass={cs.AI cs.CL cs.CV cs.MA}
}