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VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining
arXiv – CS AI|Xuanyu Zhu, Yuhao Dong, Rundong Wang, Yang Shi, Zhipeng Wu, Yinlun Peng, YiFan Zhang, Yihang Lou, Yuanxing Zhang, Ziwei Liu, Yan Bai, Yuan Zhou|
🤖AI Summary
Researchers introduce VTC-Bench, a comprehensive benchmark for evaluating multimodal AI models' ability to use visual tools for complex tasks. The benchmark reveals significant limitations in current models, with leading model Gemini-3.0-Pro achieving only 51% accuracy on multi-tool visual reasoning tasks.
Key Takeaways
- →VTC-Bench introduces 32 diverse OpenCV-based visual operations to test AI models' tool-use capabilities in realistic computer vision scenarios.
- →The benchmark includes 680 curated problems across nine cognitive categories to evaluate multi-step planning and tool composition.
- →Testing of 19 leading multimodal models reveals critical gaps in visual agentic capabilities, with top performer Gemini-3.0-Pro reaching only 51% accuracy.
- →Current AI models struggle with multi-tool composition and tend to rely on familiar functions rather than selecting optimal tools for complex tasks.
- →The research identifies fundamental challenges in AI models' ability to adapt to diverse tool-sets and generalize to unseen visual operations.
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GeminiGoogle
Read Original →via arXiv – CS AI
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