The paper titled Large Multimodal Agents: A Survey offers a comprehensive review of the evolving landscape of LLM-driven AI agents in the multimodal domain. As these agents gain the proficiency to interpret and respond to multimodal stimuli, the research categorizes the body of work into four main types and contemplates the integrated frameworks that improve the collective efficacy of multiple LMAs.

• Reviews the core components involved in LMA development.
• Compiles diverse evaluation methodologies into a universal framework.
• Highlights potential applications and future research areas.
• Aims to set a standard for effective comparisons among LMAs.

This survey serves as an essential academic compass for navigating the vast seas of LMA research, aiding researchers to harmonize methodologies and foster advancements in this dynamic field.