The paper ‘Large Multimodal Agents: A Survey’ conducts an extensive review of the ascent of large multimodal agents (LMAs), which are built upon large language models (LLMs) to handle more complex, multimodal tasks. The authors of the paper analyze the critical facets for developing LMAs, categorize research into discernible types, and discuss the variety of collaborative frameworks that enhance the overall effectiveness of LMAs.

Key Highlights:

• Identifies the cornerstones for crafting LMAs, including interaction with various modalities.
• Presents a survey of current research, differentiating four types of LMAs and their functionalities.
• Provides a comprehensive evaluation framework to better compare and understand different LMAs.
• Spotlights LMAs’ vast applications and suggests potential avenues for future inquiry.

This survey is a treasure trove of information for those interested in the converging paths of language and multimodal AI agents, helping to synthesize current knowledge and identify future challenges. It signals an important investigation into the ways multimodal tasks can be managed by AI, promoting richer and more versatile agent interactions. Researchers and aficionados can glean more by accessing the full survey.