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SpatialText: A Pure-Text Cognitive Benchmark for Spatial Understanding in Large Language Models

arXiv – CS AI|Peiyao Jiang, Zequn Qin, Xi Li|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce SpatialText, a diagnostic framework to test whether large language models can truly reason about spatial relationships or merely rely on linguistic patterns. The study reveals that current AI models fail at egocentric perspective reasoning despite proficiency in basic spatial fact retrieval.

Key Takeaways

→SpatialText framework isolates text-based spatial reasoning from visual perception to test true cognitive abilities in AI models.
→Current language models demonstrate proficiency in retrieving explicit spatial facts and global coordinate systems.
→Models exhibit critical failures in egocentric perspective transformation and local reference frame reasoning.
→Research provides evidence that models rely on linguistic co-occurrence patterns rather than constructing coherent spatial representations.
→The benchmark combines human-annotated real 3D environments with code-generated scenes to test formal spatial deduction.

#spatial-reasoning #llm-benchmarks #cognitive-ai #spatial-intelligence #ai-limitations #mental-models #arxiv-research #ai-evaluation

Read Original →via arXiv – CS AI

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