arXiv:2511.17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs). Yet, its potential in multimodal large language models (MLLMs) remains largely untapped, hindered by the absence of large-scale datasets that capture the rich, spatially grounded reasoning intrinsic to visual understanding. Existing visual-CoT resources are typically small, domain-specific, or lack the human-like stepwise structure necessary for compositional visual reasoning. In this paper, we introduce VisR
The rapid advancement of MLLMs and the recognized limitations of existing visual reasoning datasets are driving the immediate need for more sophisticated training data.
This development addresses a critical bottleneck in multimodal AI by enabling more human-like, complex visual reasoning, expanding the capabilities of MLLMs in various applications.
MLLMs will gain a significantly improved capacity for compositional visual reasoning, moving beyond simple recognition to understanding complex visual narratives and interactions.
- · AI researchers
- · Multimodal LLM developers
- · Computer vision companies
- · AI-driven automation platforms
- · Companies reliant on simple visual AI
- · Legacy AI data providers
VisReason will accelerate the development of more capable and broadly applicable MLLMs.
Improved MLLMs will enable new applications in robotics, autonomous systems, and advanced human-computer interaction.
The enhanced reasoning capabilities of MLLMs could contribute to the development of more general artificial intelligence.
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Read at arXiv cs.LG