Update README.md

README.md

@@ -7,206 +7,132 @@ metrics:
 base_model:
 - Qwen/Qwen3-VL-8B-Instruct
 pipeline_tag: image-text-to-text
-library_name: transformers
+library_name: transformers/
 tags:
 - toolcua
 - VLM
-- Compuer-Use-Agent
+- Computer-Use-Agent
 - OS-Agent
 - GUI
 - MLLM-Agent
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
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-
-#### Summary
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-
-
-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+<h1 style="
+  font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Helvetica,Arial,sans-serif;
+  font-size:48px;
+  font-weight:700;
+  line-height:1.25;
+  text-align:center;
+  margin:0 0 24px;">
+  ToolCUA-8B
+</h1>
+
+<div style="
+  display:flex;
+  justify-content:center;
+  gap:12px;
+  flex-wrap:wrap;
+  margin-bottom:28px;">
+
+  <a href="https://x-plug.github.io/ToolCUA/" style="
+     display:inline-block;
+     padding:8px 24px;
+     background:#2b2b2b;
+     color:#ffffff;
+     border-radius:36px;
+     text-decoration:none;
+     font-weight:600;
+     font-size:16px;">
+    🌐 Website
+  </a>
+
+  <a href="https://arxiv.org/abs/2508.09123" style="
+     display:inline-block;
+     padding:8px 24px;
+     background:#2b2b2b;
+     color:#ffffff;
+     border-radius:36px;
+     text-decoration:none;
+     font-weight:600;
+     font-size:16px;">
+    📑 Paper 
+  </a>
+
+  <a href="https://github.com/X-PLUG/ToolCUA" style="
+     display:inline-block;
+     padding:8px 24px;
+     background:#2b2b2b;
+     color:#ffffff;
+     border-radius:36px;
+     text-decoration:none;
+     font-weight:600;
+     font-size:16px;">
+    💻 Code
+  </a>
+
+</div>
+
+ToolCUA-8B is an end-to-end computer-use agent for orchestrating GUI actions and structured tool calls. It learns when to continue through GUI interaction, when to invoke tools, and when to switch back, enabling shorter and more reliable desktop task trajectories.
+
+<p align="center">
+  <img src="https://github.com/X-PLUG/ToolCUA/raw/main/assets/main_teaser.png" width="760" alt="ToolCUA teaser">
+</p>
+
+## Method
+
+ToolCUA uses a staged training pipeline for GUI-Tool path selection:
+
+1. Scale interleaved GUI-Tool trajectories from existing GUI-only data via trajectory-aware tool synthesis.
+2. Apply Tool-Bootstrapped GUI RFT to learn tool-calling behavior and calibrate local switching decisions.
+3. Optimize with Online Agentic RL in a GUI-Tool environment using a Tool-Efficient Path Reward for appropriate tool use and shorter execution paths.
+
+<p align="center">
+  <img src="https://github.com/X-PLUG/ToolCUA/raw/main/assets/method_overview.png" width="760" alt="ToolCUA method overview">
+</p>
+
+## Results
+
+On feasible OSWorld-MCP tasks, ToolCUA-8B reaches **46.85%** overall accuracy, **24.32%** Tool Invocation Rate (TIR), and **14.93** Average Completion Steps (ACS). Compared with Qwen3-VL-8B-Instruct, it improves accuracy by **+18.62**, improves TIR by **+15.91**, and reduces ACS by **4.41** steps.
+
+<p align="center">
+  <img src="https://github.com/X-PLUG/ToolCUA/raw/main/assets/main_results.png" width="760" alt="ToolCUA main results">
+</p>
+
+<p align="center">
+  <img src="https://github.com/X-PLUG/ToolCUA/raw/main/assets/app_results.png" width="760" alt="ToolCUA application results">
+</p>
+
+## vLLM Serve
+
+We recommend vLLM for deployment. Use `vllm>=0.12.0` and enable `--trust-remote-code`.
+
+```bash
+MAX_IMAGE=${MAX_IMAGE:-5}
+IMAGE_LIMIT_ARGS='{"image": '"$MAX_IMAGE"'}'
+PIXEL_ARGS='{"size": {"longest_edge": 3072000, "shortest_edge": 65536}}'
+
+vllm serve X-PLUG/ToolCUA-8B \
+  --trust-remote-code \
+  --max-model-len 32768 \
+  --mm-processor-kwargs "$PIXEL_ARGS" \
+  --limit-mm-per-prompt "$IMAGE_LIMIT_ARGS" \
+  --tensor-parallel-size 1 \
+  --allowed-local-media-path '/' \
+  --port 4243 \
+  --gpu-memory-utilization 0.85 \
+  --mm-processor-cache-gb 0 \
+  --no-enable-prefix-caching \
+  --enforce-eager \
+  --max-logprobs 50
+```
+
+## Citation
+
+```bibtex
+@article{hu2026toolcua,
+  title={ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents},
+  author={Hu, Xuhao and Zhang, Xi and Xu, Haiyang and Qiao, Kyle and Yang, Jingyi and Huang, Xuanjing and Shao, Jing and Yan, Ming and Ye, Jieping},
+  journal={arXiv preprint arXiv:2508.09123},
+  year={2026}
+}
+```