From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
arXiv:2606.26196v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have recently made remarkable progress in unifying vision-language understanding and reasoning, especially following the introduction of models such as OpenAI's O-series and DeepSeek's R-series, which have driven a paradigm shift toward perception-centric intelligence. However, there remains a lack of systematic surveys that examine perception from a truly unified vision-language perspective -- one that treats vision and language as an inseparable modality. Existing reviews are often fragmented, focusing
The publication of a systematic survey on Vision-Language Perception in MLLMs (Multimodal Large Language Models) indicates a maturing field moving beyond initial breakthroughs towards more unified understanding and architectural development.
This survey highlights the accelerating pace of AI development, particularly in multimodal understanding, which is critical for the next generation of AI systems that interact more naturally with the human world.
The focus is shifting from separate vision and language models to truly unified perception architectures, paving the way for more sophisticated and generalized AI capabilities.
- · AI developers
- · multimodal AI platforms
- · robotics
- · autonomous systems
- · monomodal AI companies
- · legacy AI infrastructure
Further research and development will consolidate around unified vision-language architectures, leading to more robust MLLMs.
Enterprise applications will emerge that leverage these enhanced multimodal capabilities for complex real-world tasks, potentially automating new domains.
The increased sophistication of perception-centric MLLMs could accelerate the development of general artificial intelligence and advanced AI agents capable of understanding and interacting with their environment in human-like ways.
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Read at arXiv cs.LG