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Introduction • News • Features • Model Download • Performance • Trajectory Display • Citation

Introduction

We present MindWatcher, a Tool-Integrated Reasoning (TIR) agent capable of autonomous planning, execution, and multimodal perception. Unlike traditional agents that rely on rigid workflows, MindWatcher utilizes Interleaved Thinking and Multimodal Chain-of-Thought (CoT) to flexibly switch between internal reasoning and external tool invocation at any stage.

MindWatcher addresses the limitations of current LLMs in long-tail knowledge and fine-grained visual perception. It is trained using a novel Step-wise Normalized GRPO algorithm and a hybrid reward system, abandoning traditional SFT to avoid "alignment tax" and tool abuse.

MindWatcher demonstrates SOTA performance on the newly constructed MWE-Bench and competitive results on MMSearch and SimpleVQA. We also distilled three smaller-scale models (2B, 3B, 4B) that rival larger baselines. The MWE-Bench and the distilled models will be released soon.

News

• [2025/12/30] 📑 The technical report "MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning" is available on arXiv.

Features

• 🧠 Interleaved Thinking & Multimodal CoT: The model models the reasoning process as an MDP, allowing it to "think with images" and interleave <think> and <tool_call> tokens dynamically.
• 🛠️ Comprehensive Multimodal Toolset: Equipped with expert tools including Region Cropping/Zooming, Object Grounding & Visual Search, External Text Retrieval, Webpage Content Extraction, and a Local Code Interpreter.
• ⚡ Step-wise Normalized GRPO: A customized RL algorithm that normalizes advantages based on action segments rather than global tokens, ensuring balanced supervision for both short tool commands and long reasoning chains.
• 📊 MWE-Bench: A new benchmark covering 6 categories (Vehicle, Animal, Plant, Person, Landmark, Sports) constructed via a rigorous pipeline to evaluate TIR agents.
• 📉 Efficient Distillation: We successfully distilled the capabilities of the 32B model into 2B, 3B, and 4B models, proving that strong tool-use capabilities can bridge the parameter gap.

Model Download

We are about to open-source the distilled model weights.

Performance

MindWatcher achieves state-of-the-art performance on the MWE-Bench, significantly outperforming closed-source commercial models like Gemini 2.5 Flash and GPT-5 mini in agentic settings.

MWE-Bench Results (ReAct/Agent Mode)

Method	Car	Animal	Plant	Person	Landmark	Sport	Avg.
Gemini 2.5 Flash	68.17	71.76	77.26	57.14	46.51	37.32	66.65
GPT-5 mini	61.93	68.66	81.61	44.44	57.78	80.28	69.91
Doubao-Seed-1.6-vision	57.64	57.26	61.46	65.08	38.89	39.39	57.91
Qwen2.5-VL-32B	51.74	54.13	58.19	50.79	41.11	31.69	51.41
Qwen3-VL 32B Thinking	58.98	75.50	77.83	69.84	48.89	46.48	66.95
MindWatcher-4B	56.03	84.62	87.66	68.25	41.11	36.62	69.63
MindWatcher-32B	71.31	86.04	88.92	77.78	47.78	46.48	75.35

Trajectory Display

Click each case below to expand / collapse. Only one panel is shown at a time for a slide-like browsing experience.

 1 / 4  Case 1

 2 / 4  Case 2

 3 / 4  Case 3

 4 / 4  Case 4

Citation

If you find our work helpful, please cite our paper:

@article{mindwatcher2025,
  title={MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning},
  author={MindGPT-ov Team},
  journal={arXiv preprint arXiv:2512.23412},
  year={2025}
}

Contact

For communications, please contact chenjiawei13@lixiang.com.