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Failures in Perspective-taking of Multimodal AI Systems

arXiv 2409.13929 ↗First posted 20 Sep 2024cs.AIcited by 6

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Abstract (arXiv)

This study extends previous research on spatial representations in multimodal AI systems. Although current models demonstrate a rich understanding of spatial information from images, this information is rooted in propositional representations, which differ from the analog representations employed in human and animal spatial cognition. To further explore these limitations, we apply techniques from cognitive and developmental science to assess the perspective-taking abilities of GPT-4o. Our analysis enables a comparison between the cognitive development of the human brain and that of multimodal AI, offering guidance for future research and model development.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 1 of its 4 references that are in the map. First posted on arXiv 20 Sep 2024.

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1 of 4 references.

References · 1

  1. 2024BLINK: Multimodal Large Language Models Can See but Not Perceivecited by 529

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.