arXiv:2511.07403v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but continue to struggle with spatial reasoning. Existing spatial MLLMs rely on large-scale datasets, explicit 3D inputs, architecture-specific modifications, or sparse Reinforcement Learning (RL) methods that provide insufficient guidance for spatially-grounded reasoning. We introduce SpatialThinker. To our knowledge, it is the first MLLM unifying Scene Graph Generation (SGG) and visual reasoning in a single pass via online RL. The mode
The continuous evolution of MLLMs and the increasing demand for more sophisticated, context-aware AI agents are driving this focus on enhanced spatial reasoning capabilities.
Improved spatial reasoning in MLLMs is crucial for real-world applications where understanding object relationships and environmental context directly impacts performance and reliability, moving AI closer to human-like perception.
This development proposes a method to integrate Scene Graph Generation and visual reasoning via online Reinforcement Learning, potentially reducing reliance on extensive datasets or specialized 3D inputs for spatial intelligence.
- · AI developers
- · Robotics companies
- · Augmented reality developers
- · Autonomous vehicle developers
- · Companies reliant on primitive vision systems
- · Current MLLMs with weak spatial understanding
MLLMs will exhibit significantly improved performance in tasks requiring a detailed understanding of spatial relationships between objects.
This could lead to the development of more capable and reliable AI agents for navigation, manipulation, and proactive decision-making in complex environments.
Enhanced spatial reasoning could accelerate the deployment of autonomous systems in logistics, manufacturing, and consumer robotics, transforming operational efficiency across sectors.
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Read at arXiv cs.AI