What is Multimodal AI? How LLMs Process Text, Images, and More

2026-04-06 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

Multimodal AI models are designed to ingest and/or generate multiple data modalities, such as text, images, audio, and video. Early approaches, like modular feature-level fusion, used separate encoders for different modalities (e.g., a vision encoder for images) that would extract features and pass them to a large language model. This method, while cheaper and easier to modify, risks information loss as the LLM only receives a summarized description. The current "gold standard" is native multimodality, where different data types are processed through a shared vector space. In this approach, all modalities are tokenized and embedded into the same high-dimensional space, allowing the model to reason about them simultaneously and preserve crucial details. For video, native multimodal models employ temporal reasoning by processing data in spatial-temporal patches (3D cubes) to embed motion directly into tokens. This shared vector space also enables "any-to-any generation," allowing models to take any combination of modalities as input and generate any combination as output.

Key takeaway

For AI Scientists and Machine Learning Engineers developing advanced AI systems, understanding the shift from modular feature-level fusion to native multimodality is critical. Prioritize models built on shared vector spaces for tasks requiring deep contextual understanding across modalities, especially for video, to avoid information loss and enable sophisticated any-to-any generation capabilities in your applications.

Key insights

Native multimodality uses a shared vector space for unified processing of diverse data types, enabling richer AI reasoning.

Principles

• Shared vector spaces prevent information loss.
• Temporal reasoning is key for video analysis.
• Any-to-any generation stems from shared embeddings.

Method

Native multimodality tokenizes and embeds diverse data (text, image patches, spatial-temporal video patches) into a single high-dimensional shared vector space, allowing simultaneous reasoning and coherent cross-modal generation.

In practice

• Use native multimodality for complex tasks.
• Consider feature-level fusion for cost-sensitive projects.
• Leverage spatial-temporal patches for video analysis.

Topics

• Multimodal AI
• Data Modalities
• Feature-Level Fusion
• Native Multimodality
• Shared Vector Space

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.