Survey details multimodal agent frameworks and their applications

A new survey paper explores the evolution and impact of multimodal agentic frameworks, which integrate large language models (LLMs) with diverse data types like images, audio, and video. The paper analyzes how multimodality enhances agent capabilities across perception, reasoning, planning, memory, and action. It categorizes existing systems based on their architectural choices and reviews applications in areas such as robotics, web navigation, and content generation, while also discussing performance and efficiency trade-offs. AI

IMPACT Provides a structured overview of multimodal agent frameworks, aiding researchers and developers in understanding current capabilities and future directions.

RANK_REASON The item is a survey paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• graphical user interface
• LLMs
• LMMs
• Long-form Video Understanding & Retrieval
• Multimedia Content Generation & Editing
• robotics
• Sanjoy Chowdhury
• web navigation

• paper
• other

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