From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models

2026-06-24 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

This survey addresses the evolution of unified vision-language perception in Multimodal Large Language Models (MLLMs), noting recent advancements driven by models like OpenAI's O-series and DeepSeek's R-series. It identifies a critical gap in existing literature, which often fragments vision and language perception rather than treating them as an inseparable, unified capability. To bridge this, the study formalizes MLLM perception as an intrinsic, unified vision-language capability, analogous to human innate perception. It then introduces a five-stage taxonomy that traces the paradigm evolution of MLLM perception, detailing representative methods and milestones at each phase. Finally, the survey outlines open challenges and promising research directions, aiming to provide a foundational understanding and an actionable roadmap for achieving artificial general intelligence (AGI).

Key takeaway

For AI Scientists and ML Engineers developing multimodal systems, this survey offers a crucial framework. Consult its five-stage taxonomy to contextualize current MLLM capabilities and identify promising research avenues for integrated multimodal intelligence. Use its identified challenges and future directions to guide your next-generation MLLM architecture and development efforts, aligning with the path to AGI.

Key insights

The survey unifies vision-language perception in MLLMs, proposing a five-stage evolution taxonomy and future directions for AGI.

Principles

• MLLM perception is an intrinsic, unified capability.
• Vision and language are an inseparable modality.
• A five-stage taxonomy traces MLLM perception evolution.

Method

The survey formalizes MLLM perception, introduces a five-stage taxonomy, and identifies challenges and research directions for unified multimodal intelligence.

Topics

• Multimodal Large Language Models
• Vision-Language Perception
• MLLM Taxonomy
• Artificial General Intelligence
• OpenAI O-series
• DeepSeek R-series

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.