The surge in large language models (LLMs) has opened the door to multimodal agents capable of handling complex tasks involving various forms of input. This survey delves into the multifaceted world of multimodal AI agents, including foundational strategies, integration with multiple LMAs, and challenges in standardized evaluations.

Survey Highlights

• Dissects the basic components of LMAs development.
• Reviews the research categorization into four distinct types.
• Introduces a framework to standardize LMA evaluation methods.

Impact and Future Directions

• Serves as a comprehensive resource for multimodal agent research.
• Lays out the pathway for standardizing the effectiveness measurement of LMAs.
• Suggests future explorations into multimodal applications and improvements.

This survey is essential reading for its thorough examination and forward-thinking perspective on the blossoming field of multimodal AI agents. It provides a foundation for future research and development, aiming to align various evaluations and methodologies within the community. Find further resources at this GitHub link.