Dimensions overall score 8.0

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• Perspective tokens integrated into LLaVA-1.5-13B yield state-of-the-art performance on level-2 visual perspective-taking tasks.result 85%
• Rotation-based perspective tokens generalize to non-human reference agents better than body-keypoint tokens.result 80%
• Fine-tuning enhances latent orientation sensitivity already present in the base multimodal model.technical 85%
• Multimodal language models contain precursors of allocentric reasoning but lack appropriate internal structure.technical 80%
• The approach may not scale well with increasing model sizes due to embedding complexity.limitation 75%
• Perspective tokens provide a lightweight, model-agnostic mechanism for perspective-taking.method 85%
• The method improves spatial reasoning without needing external systems.method 80%
• There is limited generalization across diverse environments beyond the tested datasets.limitation 70%

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