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New benchmark reveals MLLMs struggle with physical tool use

By PulseAugur Editorial · [2 sources] ·

Researchers have developed PhysTool-Bench, a new benchmark designed to evaluate how well Multimodal Large Language Models (MLLMs) can understand and use physical tools. The benchmark includes over 2,500 queries involving nearly 2,700 real-world tools across various industries. Testing revealed that even top-performing models struggle significantly, identifying only about 58.7% of tools and successfully completing just 21.0% of tasks, highlighting a critical gap in their ability to interact with the physical world. AI

IMPACT Highlights a significant limitation in current MLLMs for embodied AI, suggesting a bottleneck for real-world robotic applications.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhixin Ma, Yutong Zhou, Yongqi Li, Chong-Wah Ngo, Wenjie Li ·

    Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

    arXiv:2606.10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world. In such embodied settings, a central capability…

  2. arXiv cs.AI TIER_1 English(EN) · Wenjie Li ·

    Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

    Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world. In such embodied settings, a central capability is the use of physical tools, which underpins MLL…

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