MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning
Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of autonomous reasoning and tool invocation are rapidly emerging as a powerful approach for complex decision-making tasks involving multi-step interactions with external environments. In this work, we introduce MindWatcher, a TIR agent integrating interleaved thinking and multimodal chain-of-thought (CoT) reasoning. MindWatcher can autonomously decide whether and how to invoke diverse tools and coordinate their use, without relying on human prompts or workflows. The interleaved thinking paradigm enables the model to switch between thinking and tool calling at any intermediate stage, while its multimodal CoT capability allows manipulation of images during reasoning to yield more precise search results. We implement automated data auditing and evaluation pipelines, complemented by manually curated high-quality datasets for training, and we construct a benchmark, called MindWatcher-Evaluate Bench (MWE-Bench), to evaluate its performance. MindWatcher is equipped with a comprehensive suite of auxiliary reasoning tools, enabling it to address broad-domain multimodal problems. A large-scale, high-quality local image retrieval database, covering eight categories including cars, animals, and plants, endows model with robust object recognition despite its small size. Finally, we design a more efficient training infrastructure for MindWatcher, enhancing training speed and hardware utilization. Experiments not only demonstrate that MindWatcher matches or exceeds the performance of larger or more recent models through superior tool invocation, but also uncover critical insights for agent training, such as the genetic inheritance phenomenon in agentic RL.
695e39b9c679bee50ca69ab7
Jiawei Chen
claimed_verified
2026-04-09T18:52:50.894Z
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Jiawei Chen
Lost-Cloud
user
Lost-Cloud
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Xintian Shen
695e39b9c679bee50ca69ab9
Lihao Zheng
695e39b9c679bee50ca69aba
Zhenwei Shao
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Hongyuan Zhang
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Pengfei Yu
claimed_verified
2026-03-26T14:05:01.079Z
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Yu
pengfei2025
user
pengfei2025
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Xudong Rao
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Ning Mao
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Xiaobo Liu
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Lian Wen
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Chaoqun Du
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Feng Gu
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Wei He
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Qizhen Li
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Shanshan Li
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Zide Liu
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Jing Luo
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Lifu Mu
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Xuhao Pan
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Chang Ren
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Haoyi Sun
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Qian Wang
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Wei Wang
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Hongfu Yang
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Jiqing Zhan
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Chunpeng Zhou
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Zheng Zhou
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Hao Ma
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Tao Wei
695e39b9c679bee50ca69ad4
Pan Zhou
695e39b9c679bee50ca69ad5
Wei Chen
MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning
Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of autonomous reasoning and tool invocation are rapidly emerging as a powerful approach for complex decision-making tasks involving multi-step interactions with external environments. In this work, we introduce MindWatcher, a TIR agent integrating interleaved thinking and multimodal chain-of-thought (CoT) reasoning. MindWatcher can autonomously decide whether and how to invoke diverse tools and coordinate their use, without relying on human prompts or workflows. The interleaved thinking paradigm enables the model to switch between thinking and tool calling at any intermediate stage, while its multimodal CoT capability allows manipulation of images during reasoning to yield more precise search results. We implement automated data auditing and evaluation pipelines, complemented by manually curated high-quality datasets for training, and we construct a benchmark, called MindWatcher-Evaluate Bench (MWE-Bench), to evaluate its performance. MindWatcher is equipped with a comprehensive suite of auxiliary reasoning tools, enabling it to address broad-domain multimodal problems. A large-scale, high-quality local image retrieval database, covering eight categories including cars, animals, and plants, endows model with robust object recognition despite its small size. Finally, we design a more efficient training infrastructure for MindWatcher, enhancing training speed and hardware utilization. Experiments not only demonstrate that MindWatcher matches or exceeds the performance of larger or more recent models through superior tool invocation, but also uncover critical insights for agent training, such as the genetic inheritance phenomenon in agentic RL.
MindWatcher is a tool-integrated reasoning agent that combines interleaved thinking and multimodal chain-of-thought reasoning to autonomously perform complex decision-making tasks through coordinated tool invocation without human prompts.
tool-integrated reasoning interleaved thinking multimodal chain-of-thought agent training genetic inheritance phenomenon agentic RL