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Does Spatial Cognition Emerge in Frontier Models?

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arxiv 2410.06468 v2 pith:YO3YJBVE submitted 2024-10-09 cs.AI cs.CVcs.LG

Does Spatial Cognition Emerge in Frontier Models?

classification cs.AI cs.CVcs.LG
keywords modelsspatialbenchmarkcognitionfrontiercognitiveevaluateslarge
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Not yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate parallel presentations via text and images, allowing us to benchmark both large language models and large multimodal models. Results suggest that contemporary frontier models fall short of the spatial intelligence of animals, performing near chance level on a number of classic tests of animal cognition. Code and data are available: https://github.com/apple/ml-space-benchmark

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    A new diagnostic benchmark decomposes LLM spatial navigation into three cognitive scales and shows that cross-scale aggregation, not single-level deficits, causes failure beyond small mazes.

  2. SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes

    cs.CV 2026-05 unverdicted novelty 7.0

    SpatialAct benchmark shows VLMs handle isolated spatial reasoning but fail to maintain coherent spatial beliefs and produce reliable actions in multi-turn 3D interactions, underperforming humans.

  3. VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations

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  4. CVSBench: A Comprehensive Benchmark for Cross-view Spatial Reasoning and Dreaming

    cs.CV 2026-06 unverdicted novelty 6.0

    CVSBench benchmark shows VLMs struggle with cross-view spatial consistency but improve substantially when given 3D scene imagination inputs.

  5. Spatio-Temporal Grounding of Large Language Models from Perception Streams

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    FESTS uses Spatial Regular Expressions compiled from queries to generate 27k training tuples that raise a 3B-parameter LLM's frame-level F1 on spatio-temporal video reasoning from 48.5% to 87.5%, matching GPT-4.1 whil...

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    MLLMs achieve competitive but subhuman performance on the new VSI-Bench for visual-spatial intelligence from videos, with spatial reasoning as the main bottleneck and explicit cognitive map generation improving distan...

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