Scaling Spatial Reasoning in MLLMs through Programmatic Data Synthesis
Published:Dec 18, 2025 06:30
•1 min read
•ArXiv
Analysis
This article, sourced from ArXiv, likely presents a research paper focusing on improving the spatial reasoning capabilities of Multimodal Large Language Models (MLLMs). The core approach involves using programmatic data synthesis, which suggests generating training data algorithmically rather than relying solely on manually curated datasets. This could lead to more efficient and scalable training for spatial tasks.
Key Takeaways
Reference
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