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This is a Quantitative Study study.

October 8, 2025Open Access

LEGO Co-builder: Exploring Fine-Grained Vision-Language Modeling for Multimodal LEGO Assembly Assistants

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Benchmark assesses object detection and instruction-following in multimodal LEGO assembly, indicating gaps in fine-grained understanding.

The study reveals that advanced vision-language models struggle with fine-grained assembly tasks, showing limitations in their spatial reasoning capabilities.
Results showed a maximum F1 score of only 40.54% in state detection, demonstrating significant challenges faced by even state-of-the-art models.
A unified framework was introduced for evaluating multimodal instruction-following, merging real-world LEGO assembly with generated scenes.
The released dataset and codebase aim to foster further research on advancing multimodal assembly assistants based on real-world workflows.

Huang et al. (2025) studied this question.

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