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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.23412 |
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| _version_ | 1866911357439311872 |
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| author | Chen, Jiawei Shen, Xintian Zheng, Lihao Shao, Zhenwei Cui, Handong Du, Chaoqun Gong, Li Gu, Feng Hao, Xuefeng He, Wei He, Jiabang Hu, Yi Huang, Bin Li, Shanshan Li, Qizhen Luo, Jing Liu, Zide Liu, Xiaobo Mao, Ning Mu, Lifu Pan, Xuhao Qu, Zhiheng Ren, Chang Rao, Xudong Sun, Haoyi Wang, Qian Wang, Shuai Wang, Zhichao Wang, Wei Wen, Lian Zhan, Jiqing Yang, Hongfu Yang, Sheng Yang, Jiajun Yu, Pengfei Zhang, Hongyuan Zhang, Bin Zhou, Chunpeng Zhou, Zheng Zhou, Shucheng Xie, Shuo Zhu, Yun Ma, Hao Wei, Tao Zhou, Pan Chen, Wei |
| author_facet | Chen, Jiawei Shen, Xintian Zheng, Lihao Shao, Zhenwei Cui, Handong Du, Chaoqun Gong, Li Gu, Feng Hao, Xuefeng He, Wei He, Jiabang Hu, Yi Huang, Bin Li, Shanshan Li, Qizhen Luo, Jing Liu, Zide Liu, Xiaobo Mao, Ning Mu, Lifu Pan, Xuhao Qu, Zhiheng Ren, Chang Rao, Xudong Sun, Haoyi Wang, Qian Wang, Shuai Wang, Zhichao Wang, Wei Wen, Lian Zhan, Jiqing Yang, Hongfu Yang, Sheng Yang, Jiajun Yu, Pengfei Zhang, Hongyuan Zhang, Bin Zhou, Chunpeng Zhou, Zheng Zhou, Shucheng Xie, Shuo Zhu, Yun Ma, Hao Wei, Tao Zhou, Pan Chen, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning Chen, Jiawei Shen, Xintian Zheng, Lihao Shao, Zhenwei Cui, Handong Du, Chaoqun Gong, Li Gu, Feng Hao, Xuefeng He, Wei He, Jiabang Hu, Yi Huang, Bin Li, Shanshan Li, Qizhen Luo, Jing Liu, Zide Liu, Xiaobo Mao, Ning Mu, Lifu Pan, Xuhao Qu, Zhiheng Ren, Chang Rao, Xudong Sun, Haoyi Wang, Qian Wang, Shuai Wang, Zhichao Wang, Wei Wen, Lian Zhan, Jiqing Yang, Hongfu Yang, Sheng Yang, Jiajun Yu, Pengfei Zhang, Hongyuan Zhang, Bin Zhou, Chunpeng Zhou, Zheng Zhou, Shucheng Xie, Shuo Zhu, Yun Ma, Hao Wei, Tao Zhou, Pan Chen, Wei Artificial Intelligence 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. |
| title | MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.23412 |