Core Insights - The article discusses the limitations of current multimodal large language models (MLLMs) in spatial intelligence, highlighting that even advanced models struggle with basic spatial tasks that children can perform easily [2][5] - A new approach is proposed, focusing on geometric problems as a means to enhance spatial perception and reasoning in vision-language models [6][8] Group 1: Limitations of Current Models - Despite significant advancements, state-of-the-art MLLMs still lack true spatial intelligence, often making errors in tasks like counting objects or identifying nearby items [2][5] - Over 70% of errors in spatial reasoning tasks stem from the models' inability to infer spatial phenomena rather than deficiencies in visual recognition or language processing [5] Group 2: Proposed Solutions - The research team aims to improve model performance by learning from a broader range of spatial phenomena, moving beyond single dataset limitations [5][8] - The study introduces a new dataset, Euclid30K, containing 29,695 geometric problems, which is designed to enhance the models' spatial reasoning capabilities [12][13] Group 3: Geometric Problems as Proxies - Solving geometric problems requires skills such as shape recognition, spatial relationship inference, and multi-step logical reasoning, which are also essential for spatial perception tasks [10] - Evidence from educational psychology suggests a strong correlation between geometric problem-solving and spatial intelligence, indicating that targeted practice can enhance spatial abilities [10] Group 4: Dataset Characteristics - The Euclid30K dataset includes a diverse range of geometric problems, with a total of 29,695 questions, including 18,577 plane geometry and 11,118 solid geometry questions [13] - The dataset was meticulously curated to ensure high quality, with answers verified for accuracy [12][13] Group 5: Model Training and Results - The models were trained using standard GRPO methods, and results showed performance improvements across various benchmarks after training with geometric problems [15][17] - A causal ablation study confirmed that the performance gains were attributable to the geometric tasks rather than other factors like algorithm design or data volume [17]