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Multimodal Reasoning and Planning: The Next Frontier

While LLMs are primarily text-based, the latest Multimodal LLMs (MLLMs) like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro are beginning to demonstrate visual reasoning and spatial planning.

What is Multimodal Reasoning?

Multimodal reasoning is the ability of an AI to “see” and solve a problem:

  • Spatial Understanding: Given an image of a desk, where should the laptop be placed?
  • Sequence Planning: If an agent is in a kitchen, how can it prepare a meal?
  • Interactive Agents: MLLMs can control browsers or desktops by “seeing” the screen and selecting elements to click.

The Future of Physical Agents

Multimodal reasoning will be critical for robots:

  1. Environmental Learning: Robots can use MLLMs to understand their surroundings and adapt to new tasks.
  2. Object Recognition: MLLMs can identify and categorize thousands of objects in real-time.
  3. Complex Instructions: We can give robots natural language instructions like “Clean up the kitchen,” and they will figure out the steps themselves.
  • Real-time Performance: Models must be fast and responsive to work in real-time.
  • Data Limitations: We need more high-quality multimodal data to train these models.
  • Safety and Ethics: As robots become more autonomous, we must ensure they are safe and ethically behaved.