arXiv:2601.06521v2 Announce Type: replace-cross Abstract: While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile visual understanding. We uncovered a crucial fact: state-of-the-art MLLMs consistently fail on basic visual tasks that humans, even 3-year-olds, can solve effortlessly. To systematically investigate this gap, we introduce BabyVision, a benchmark designed to assess core visual abilities independent of linguistic knowledge for MLLMs. BabyVision spans a wide ran
The rapid advancement and widespread deployment of Multimodal LLMs have exposed fundamental limitations in their visual reasoning capabilities, prompting a focused effort to address these shortcomings.
This research highlights a critical gap in current AI, suggesting that MLLMs lack foundational visual intelligence comparable to humans, which is necessary for truly robust and reliable AI systems.
The introduction of benchmarks like BabyVision will drive research toward developing MLLMs with more human-like visual understanding, shifting focus from language-centric to perception-centric training.
- · AI researchers in visual perception
- · Developers of robust MLLMs
- · Sectors requiring high-fidelity visual AI (e.g., robotics, autonomous vehicles)
- · MLLMs heavily reliant on language priors
- · Applications deploying visually fragile MLLMs without proper validation
- · Developers overlooking fundamental visual understanding
New architectural approaches and training paradigms will emerge to tackle the visual reasoning gap identified by BabyVision.
Improved visual reasoning will lead to more capable and trustworthy AI for complex real-world tasks, reducing reliance on explicit linguistic instructions for perception.
The pursuit of 'pre-linguistic' AI intelligence could lead to a deeper understanding of human cognition and new pathways for AI general intelligence.
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Read at arXiv cs.CL