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WebWatcher AI: Revolutionizing Multimodal Research with Advanced Visual-Language Reasoning

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WebWatcher: The New Frontier in Vision-Language AI Research Agents

Have you ever wished for an assistant that could not only understand images but also reason through complex problems, use various tools, and actively gather information from the internet? What sounds like science fiction is now reality with WebWatcher—a truly multimodal AI agent that represents a significant leap forward in artificial intelligence research.

This isn’t just another “image captioning” AI. WebWatcher is an advanced research assistant with enhanced visual-language reasoning capabilities and multi-tool interaction functionality. Whether you’re a researcher, engineer, or simply someone interested in cutting-edge AI applications, understanding WebWatcher’s capabilities will provide valuable insights into the future of artificial intelligence.

What Exactly is WebWatcher?

WebWatcher is a multimodal agent specifically designed for deep research tasks. The term “multimodal” means it can simultaneously understand and process both image and text information, while “agent” signifies that it doesn’t just passively answer questions—it actively plans, uses tools, and executes multi-step operations to complete complex tasks.

Imagine encountering a complex diagram or real-world scene image. You might need to not only understand what’s in the image but also query relevant information, perform reasoning, or even write code to analyze data. WebWatcher was created specifically for these types of challenges.

Three Breakthrough Capabilities of WebWatcher

1. A New Benchmark: BrowseComp-VL

Advancing any field requires reliable ways to measure progress. The research team behind WebWatcher developed a new benchmark called BrowseComp-VL specifically to evaluate multimodal agents.

This benchmark focuses on assessing deep reasoning and strategic planning capabilities. Unlike traditional visual question-answering datasets, BrowseComp-VL features tasks that are more complex and better reflect real-world information needs. It requires agents not only to “see” image content but also to know how to actively gather information, integrate knowledge, and ultimately make decisions.

2. Automated Trajectory Generation Pipeline

Training an agent that can effectively use multiple tools presents a significant challenge: obtaining high-quality training data. WebWatcher addresses this through an innovative approach—an automated pipeline that generates multi-step reasoning trajectories.

These trajectories simulate human decision-making processes when using tools, including when to perform web searches, when to visit specific pages, and when to call upon code interpreters. This data is used not only for initial model training but also further optimized through reinforcement learning, making WebWatcher’s tool usage more precise and efficient.

The tools available to WebWatcher include:

  • ✦ Web Image Search
  • ✦ Web Text Search
  • ✦ Webpage Visit
  • ✦ Code Interpreter
  • ✦ Built-in OCR Tool

3. Exceptional Performance Achievements

WebWatcher has demonstrated leading performance across multiple challenging visual question-answering benchmarks, including:

  • ✦ Humanity’s Last Exam (HLE)-VL: Focused on multi-step complex reasoning
  • ✦ BrowseComp-VL: Comprehensive visual-language reasoning challenges
  • ✦ LiveVQA: Real-time visual question answering
  • ✦ MMSearch: Multimodal information retrieval tasks

Specifically, the WebWatcher-32B model achieved an average score of 18.2% on HLE, surpassing the GPT-4o-based OmniSearch baseline. On LiveVQA and MMSearch, it achieved scores of 58.7% and 55.3% respectively, demonstrating both stability and superiority in real-world visual search tasks.

Detailed Performance Analysis

1. Complex Reasoning Capabilities (HLE-VL)

On the HLE-VL benchmark designed for multi-step complex reasoning, WebWatcher achieved a leading Pass@1 score of 13.6%, significantly outperforming representative models including GPT-4o (9.8%), Gemini2.5-flash (9.2%), and Qwen2.5-VL-72B (8.6%).

2. Information Retrieval Capabilities (MMSearch)

In the MMSearch evaluation, WebWatcher demonstrated exceptional retrieval accuracy with a Pass@1 score of 55.3%, substantially surpassing Gemini2.5-flash (43.9%) and GPT-4o (24.1%). This shows superior precision in retrieval tasks and robust information aggregation capabilities in complex scenarios.

3. Knowledge-Retrieval Integration (LiveVQA)

On the LiveVQA benchmark, WebWatcher achieved a Pass@1 score of 58.7%, outperforming Gemini2.5-flash (41.3%), Qwen2.5-VL-72B (35.7%), and GPT-4o (34.0%).

4. Information Optimization and Aggregation (BrowseComp-VL)

On the most comprehensively challenging BrowseComp-VL benchmark, WebWatcher dominated with an average score of 27.0%—more than doubling the performance of mainstream models including GPT-4o (13.4%), Gemini2.5-flash (13.0%), and Claude-3.7 (11.2%).

Getting Started with WebWatcher

If you’re interested in exploring WebWatcher’s capabilities firsthand, follow these step-by-step instructions.

Step 1: Download the Model

You can download the WebWatcher model through the Hugging Face platform:

🤗 HuggingFace Download Link

Step 2: Data Preparation

Before running inference, you need to download test set images to the infer/scripts_eval/images folder. This can be accomplished by running the infer/scripts_eval/download_image.py script.

If you encounter issues downloading images from the provided OSS URLs, you can obtain the images from the original dataset source and manually place them in the corresponding folder.

Step 3: Running Inference

Run the infer/scripts_eval/scripts/eval.sh script with the following required parameters:

Parameter Description
benchmark Name of dataset to test. Options: 'hle', 'gaia', 'livevqa', 'mmsearch', 'simplevqa', 'bc_vl_v1', 'bc_vl_v2'
EXPERIMENT_NAME User-defined experiment name
MODEL_PATH Path to trained model
DASHSCOPE_API_KEY GPT API key
IMG_SEARCH_KEY Google SerpApi key (for image search)
JINA_API_KEY Jina API key
SCRAPERAPI_KEY Scraper API key
QWEN_SEARCH_KEY Google SerpApi key (for text search)

Note: If you need to upload searched images to OSS, you also need to provide:

  • ✦ ALIBABA_CLOUD_ACCESS_KEY_ID: Alibaba Cloud OSS access key ID
  • ✦ ALIBABA_CLOUD_ACCESS_KEY_SECRET: Alibaba Cloud OSS access key secret

Step 4: Evaluating Results

Run the infer/vl_search_r1/pass3.sh script to use LLM-as-judge for evaluating Pass@3 and Pass@1 metrics. Required parameters:

  • ✦ DIRECTORY: Path to folder containing JSONL files generated from inference
  • ✦ DASHSCOPE_API_KEY: GPT API key

Frequently Asked Questions

What exactly is WebWatcher?

WebWatcher is a multimodal AI agent with advanced visual-language reasoning capabilities and multi-tool interaction functionality, specifically designed for deep research tasks.

How is WebWatcher different from traditional visual question-answering systems?

Traditional visual QA systems typically only answer questions based on given images and text. WebWatcher can actively use multiple tools (such as web search, code interpreters, etc.) for multi-step reasoning and information gathering, functioning more like a human research assistant.

What tasks does WebWatcher perform exceptionally well?

WebWatcher excels across multiple challenging benchmarks, particularly in tasks requiring complex reasoning, information retrieval, and knowledge integration, such as HLE-VL, BrowseComp-VL, LiveVQA, and MMSearch.

How can I run WebWatcher locally?

You need to download the model, prepare test data, and then run inference and evaluation according to the provided scripts. Specific steps can be found in the “Getting Started with WebWatcher” section above.

What API keys are required to run WebWatcher?

Running WebWatcher requires several API keys, including DashScope API key, Google SerpApi keys (for image and text search), Jina API key, and Scraper API key. If you need to upload images to OSS, you also need Alibaba Cloud OSS access keys.

Citation

If you find WebWatcher helpful for your research, please cite the following paper:

@article{geng2025webwatcher,
  title={WebWatcher: Breaking New Frontiers of Vision-Language Deep Research Agent},
  author={Geng, Xinyu and Xia, Peng and Zhang, Zhen and Wang, Xinyu and Wang, Qiuchen and Ding, Ruixue and Wang, Chenxi and Wu, Jialong and Zhao, Yida and Li, Kuan and others},
  journal={arXiv preprint arXiv:2508.05748},
  year={2025}
}

Conclusion

WebWatcher represents a new direction in the development of multimodal AI agents. It not only represents significant technical breakthroughs but, more importantly, provides a viable path toward building practically useful artificial intelligence research assistants. As the technology continues to mature, we can reasonably expect that such agents will play increasingly important roles in scientific research, data analysis, knowledge discovery, and other fields.

Whether you’re a researcher, developer, or simply an observer interested in AI technology, WebWatcher deserves your attention and experimentation. It may only represent the beginning of future intelligent assistants, but it already demonstrates the exciting potential of artificial intelligence in understanding and interaction.

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