The foundations and limits of multimodal agentic frameworks compiled

Summary

This 16-author survey, accepted at TMLR, systematically examines how multimodal data—including visual, audio, and video—shapes the core modules of agentic frameworks (perception, reasoning, planning, memory, and action). By organizing the literature through a module-centric taxonomy, the work covers a broad spectrum of applications, ranging from robotics to long-form video understanding.

Why it matters

Agentic AI is moving beyond large language models (LLMs) limited to text, evolving into systems that perceive and act within the physical world. Consequently, understanding how different modalities integrate into the agent loop is critical for both researchers and practitioners. The survey fills a gap by treating multimodality not as an add-on feature, but as a structural design variable within each functional module. It also explicitly addresses the efficiency-scalability trade-offs that will determine real-world deployment.

Highlights

• A module-centric taxonomy that links architectural choices (delegation, late fusion, early fusion) to specific agent capabilities
• Coverage of four application domains: Robotics, GUI/Web Navigation, Multi-Environment Content Generation, and Long-Form Video Understanding
• Explicit analysis of efficiency-scalability trade-offs through training/inference costs, latency, and deployment constraints

Critical take

With its 16 authors and scope spanning from robotics to video search, the study risks superficial coverage rather than providing a deep critical analysis of where multimodal agentic architectures fail in practice. Furthermore, as long as sub-systems remain fragile and domain-specific, the 'roadmap' framework may be more aspirational than actionable.