Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly
arXiv:2605.21625v1 Announce Type: cross Abstract: The emergence of Large Vision-Language Models (LVLMs) has significantly advanced video understanding capabilities. However, existing benchmarks focus predominantly on coarse-grained tasks such as action segmentation, classification, captioning, and retrieval. Furthermore, these benchmarks often rely on entities that can be easily identified verbally, like household objects, animals, human subjects, etc., limiting their applicability to complex, in-the-wild video scenarios. But, many applications such as furniture assembly, cooking, etc., requir
This research addresses the current limitations of Large Vision-Language Models (LVLMs) in understanding complex, fine-grained tasks at a time when their deployment in real-world scenarios is increasing.
Improving spatio-temporal understanding in LVLMs is crucial for their application in intricate physical tasks, enhancing their utility across various industries and accelerating autonomous agent development.
The development of benchmarks like 'Flat-Pack Bench' will enable more rigorous evaluation of LVLMs, pushing models towards capabilities beyond coarse-grained recognition to detailed interaction within physical spaces.
- · AI researchers
- · Robotics companies
- · E-commerce & logistics platforms
- · AI Agent developers
- · Companies relying on limited LVLM capabilities
- · Manual assembly industries (long-term)
LVLMs will gain more sophisticated abilities to understand and interact with complex physical environments.
This improved understanding will accelerate the development and deployment of more capable autonomous AI agents and robots in manufacturing and service industries.
Advanced agentic systems, fluent in complex spatio-temporal reasoning, could eventually redefine a broad spectrum of skilled manual and cognitive labor.
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