  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.
