PlanarBench: Evaluating LLM Spatial Reasoning via Planar Graph Drawing

1. 1Build a planar graph dataset using edge lists and node labels
2. 2Run PlanarBench on a set of LLM models to evaluate their spatial reasoning capabilities
3. 3Configure the evaluation metrics to prioritize edge count as a difficulty predictor
4. 4Test the correlation between edge count and model performance
5. 5Apply the findings to improve LLM graph drawing capabilities

AI engineers and researchers can benefit from this micro-lesson to improve their understanding of LLM spatial reasoning capabilities, and apply this knowledge to develop more robust models

💡 Edge count is a dominant predictor of difficulty in LLM spatial reasoning tasks, with a correlation coefficient of -0.85

🤖 PlanarBench evaluates LLM spatial reasoning via planar graph drawing! 💡