TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning
Quick summary
arXiv:2601.16520v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual recognition and semantic understanding, yet precise compositional spatial reasoning under geometric constraints remains underexplored. Existing benchmarks mainly assess coarse spatial relations and rarely support rigorous geometric verification or multiple valid solutions in constructive tasks. To address these limitations, we introduce TangramPuzzle, a benchmark comprising 668 validated configurations and 1,336 instances for evaluating compositional sp
Key takeaways
- arXiv:2601.16520v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual recognition and semantic understanding, yet precise compositional spatial reasoning under geometric constraints remains underexplored.
- Existing benchmarks mainly assess coarse spatial relations and rarely support rigorous geometric verification or multiple valid solutions in constructive tasks.
- To address these limitations, we introduce TangramPuzzle, a benchmark comprising 668 validated configurations and 1,336 instances for evaluating compositional sp
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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