Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation

The project addresses a known weakness in current MLLMs: spatial reasoning. While the benchmark fills a specific niche (mathematical 2D/3D relations), its defensibility is low because it is a static evaluation set. 19 forks against 0 stars in just 9 days suggests this may be part of an academic challenge or a coordinated research release, but it lacks the 'data gravity' of established benchmarks like MMMU or MathVista. Frontier labs (OpenAI, Google, Anthropic) are heavily incentivized to solve spatial reasoning for applications in robotics and world-modeling (e.g., Sora, Gemini 1.5 Pro); they likely have internal benchmarks that are significantly more comprehensive. The project is at high risk of being 'solved' or superseded by the next generation of models (GPT-5/Gemini 2.0) within 6 months, rendering the specific dataset obsolete as a differentiator. Its value lies primarily in highlighting the gap to the research community rather than providing a long-term moat.