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MLLMs Fail to Refuse when Using Tools Agentically

Agentic multimodal large language models (MLLMs) have recently pushed the frontier of visual reasoning by calling tools such as zooming and tagging. Despite the recent strong success of agentic MLLMs, this work uncovers a critical safety failure in the tool-use paradigm: agentic tool-using MLLMs…

By Rikiya Takehi, Ryo Hachiuma, Shaona Ghosh, Dan Zhao, Yu-Chiang Frank Wang, Yusuke HirotaarXiv - cs.AI

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Originally published by arXiv - cs.AI on Oct 6, 2026.

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