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New VA-Bench reveals multimodal AI struggles with robot arm tasks

By PulseAugur Editorial · [1 sources] ·

A new benchmark called VA-Bench, developed by Dalian University of Technology, evaluates multimodal AI models on robot arm tasks. The top-performing model, Qwen3.8-max, managed to complete just over half of the tasks, highlighting the current limitations of AI in complex, long-horizon operations. AI

IMPACT Highlights current limitations in multimodal AI for complex robotic tasks, indicating areas for future development.

RANK_REASON The cluster describes a new benchmark and its results for evaluating AI models, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New VA-Bench reveals multimodal AI struggles with robot arm tasks

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The cluster describes a new benchmark and its results for evaluating AI models, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Pandaily TIER_1 English(EN) · [email protected] (Pandaily) ·

    Dalian University of Technology's VA-Bench: Top Multimodal Models Finish Only Half of Robot Tasks

    VA-Bench from Dalian University of Technology tests 12 multimodal model setups on robot-arm tasks; Qwen3.8-max leads at 53.93%, and none finishes a strict long-horizon task.

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