MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning
Authors: name: Jiawei Chen, name: Xintian Shen, name: Lihao Zheng, name: Zhenwei Shao, name: Hongyuan Zhang, name: Pengfei Yu, name: Xudong Rao, name: Ning Mao, name: Xiaobo Liu, name: Lian Wen, name: Chaoqun Du, name: Feng Gu, name: Wei He, name: Qizhen Li, name: Shanshan Li, name: Zide Liu, name: Jing Luo, name: Lifu Mu, name: Xuhao Pan, name: Chang Ren, name: Haoyi Sun, name: Qian Wang, name: Wei Wang, name: Hongfu Yang, name: Jiqing Zhan, name: Chunpeng Zhou, name: Zheng Zhou, name: Hao Ma, name: Tao Wei, name: Pan Zhou, name: Wei Chen
Organization: LiAuto Foundation Model

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.