Towards Rationality in Language and Multimodal Agents: A Survey

Rationality is the quality of being guided by reason, characterized by decision-making that aligns with evidence and logical principles. It plays a crucial role in reliable problem-solving by ensuring well-grounded and consistent solutions. While large language models (LLMs) have made significant progress in generating human-like text, they still exhibit limitations such as bounded knowledge space and inconsistent outputs. In response, recent efforts have shifted toward developing multimodal and multi-agent systems, as well as integrating modules like external tools, programming codes, symbolic reasoners, utility function, and conformal risk controls rather than relying solely on a single LLM for decision-making. This paper surveys the state-of-the-art advancements in language and multimodal agents, evaluates how they contribute to make intelligent agents more rational, and identifies open challenges and future research directions. We maintain an open repository at https://github.com/bowen-upenn/Agent_Rationality.

@article{jiang2025_2406.00252,
  title={ Towards Rationality in Language and Multimodal Agents: A Survey },
  author={ Bowen Jiang and Yangxinyu Xie and Xiaomeng Wang and Yuan Yuan and Zhuoqun Hao and Xinyi Bai and Weijie J. Su and Camillo J. Taylor and Tanwi Mallick },
  journal={arXiv preprint arXiv:2406.00252},
  year={ 2025 }
}