Improve model card: Associate with WebWeaver paper and add Quick Start

This PR updates the model card for the `Tongyi-DeepResearch-30B-A3B` model to accurately reflect its association with the [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://huggingface.co/papers/2509.13312) paper.

Specifically, it:
- Links directly to the associated WebWeaver paper.
- Adds explicit links to the project blog and GitHub repository for easy access.
- Incorporates a detailed "Quick Start" section with environment setup, installation, and inference instructions, directly sourced from the GitHub repository.
- Updates the "Model Download" section with a clear table of download links.
- Includes relevant benchmark results and a list of related papers from the Deep Research Agent Family.
- Adds comprehensive "News", "Misc", "Talent Recruitment", and "Contact Information" sections from the GitHub README.
- Adds a BibTeX citation for the WebWeaver paper alongside the existing model citation.

These changes enhance the model card's completeness, usability, and discoverability.

README.md

@@ -1,14 +1,26 @@
 ---
-license: apache-2.0
 language:
 - en
-pipeline_tag: text-generation
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 ---
 
-# Introduction
+# Tongyi DeepResearch: WebWeaver
+
+This repository contains **Tongyi DeepResearch**, an agentic large language model, which is associated with the paper [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://huggingface.co/papers/2509.13312).
+
+<div align="center" style="line-height: 1;">
 
-We present  **Tongyi DeepResearch**, an agentic large language model featuring 30 billion total parameters, with only 3 billion activated per token. Developed by Tongyi Lab, the model is specifically designed for **long-horizon, deep information-seeking** tasks. Tongyi-DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch and FRAMES.
+[![Paper](https://img.shields.io/badge/Paper-WebWeaver-2b9348.svg?logo=arXiv)](https://huggingface.co/papers/2509.13312) |
+[![GitHub](https://img.shields.io/badge/Github-24292F?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Alibaba-NLP/DeepResearch) |
+[![Blog](https://img.shields.io/badge/Blog-4285F4?style=for-the-badge&logo=google-chrome&logoColor=white)](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)
+
+</div>
+
+## Introduction
+
+We present **Tongyi DeepResearch**, an agentic large language model featuring 30 billion total parameters, with only 3 billion activated per token. Developed by Tongyi Lab, the model is specifically designed for **long-horizon, deep information-seeking** tasks. Tongyi-DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch and FRAMES.
 
 More details can be found in our 📰 [Tech Blog](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research).
 
@@ -21,12 +33,128 @@ More details can be found in our 📰 [Tech Blog](https://tongyi-agent.github.io
 - 🔁 **End-to-end reinforcement learning**: We employ a strictly on-policy RL approach based on a customized Group Relative Policy Optimization framework, with token-level policy gradients, leave-one-out advantage estimation, and selective filtering of negative samples to stabilize training in a non‑stationary environment.
 - 🤖 **Agent Inference Paradigm Compatibility**: At inference, Tongyi-DeepResearch is compatible with two inference paradigms: ReAct, for rigorously evaluating the model's core intrinsic abilities, and an IterResearch-based 'Heavy' mode, which uses a test-time scaling strategy to unlock the model's maximum performance ceiling.
 
-## Download
+# Model Download
+
+You can directly download the model by following the links below.
+
+| Model             | Download Links                            | Model Size | Context Length |
+| :-----------------: | :-----------------------------------------: | :----------: | :--------------: |
+| Tongyi-DeepResearch-30B-A3B | [🤗 HuggingFace](https://huggingface.co/Alibaba-NLP/Tongyi-DeepResearch-30B-A3B)<br> [🤖 ModelScope](https://modelscope.cn/models/iic/Tongyi-DeepResearch-30B-A3B) | 30B-A3B | 128K |
+
+# News
+
+[2025/09/17]🔥 We have released **Tongyi-DeepResearch-30B-A3B**.
+
+# Deep Research Benchmark Results
+<p align="center">
+  <img width="100%" src="https://github.com/Alibaba-NLP/DeepResearch/raw/main/assets/benchmark.png">
+</p>
+
+## Quick Start
+
+This guide provides instructions for setting up the environment and running inference scripts located in the [inference](https://github.com/Alibaba-NLP/DeepResearch/tree/main/inference/) folder.
+
+### 1. Environment Setup
+- Recommended Python version: **3.10.0** (using other versions may cause dependency issues).
+- It is strongly advised to create an isolated environment using `conda` or `virtualenv`.
+
+```bash
+# Example with Conda
+conda create -n react_infer_env python=3.10.0 
+conda activate react_infer_env
+```
+
+### 2. Installation
+
+Install the required dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Prepare Evaluation Data
+- Create a folder named `eval_data/` in the project root.
+- Place your QA file in **JSONL** format inside this directory, e.g. `eval_data/example.jsonl`.
+- Each line must be a JSON object that includes **both** of the following keys:
+  ```json
+  {"question": "...","answer": "..."}
+  ```
+- A sample file is provided in the `eval_data` folder for reference.
+- If you plan to use the *file parser* tool, **prepend the file name to the `question` field** and place the referenced file inside the `eval_data/file_corpus/` directory.
+
+### 4. Configure the Inference Script
+- Open `run_react_infer.sh` and modify the following variables as instructed in the comments:
+  * `MODEL_PATH`  - path to the local or remote model weights.
+  * `DATASET`     - path to the evaluation set, e.g. `example`.
+  * `OUTPUT_PATH` - path for saving the prediction results, e.g. `./outputs`.
+- Depending on the tools you enable (retrieval, calculator, web search, etc.), provide the required `API_KEY`, `BASE_URL`, or other credentials. Each key is explained inline in the bash script.
 
-You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/DeepResearch.
 
+### 5. Run the Inference Script
 
+```bash
+bash run_react_infer.sh
+```
+---
+
+With these steps, you can fully prepare the environment, configure the dataset, and run the model. For more details, consult the inline comments in each script or open an issue.
+
+## Benchmark Evaluation
+
+We provide benchmark evaluation scripts for various datasets. Please refer to the [evaluation scripts](https://github.com/Alibaba-NLP/DeepResearch/tree/main/evaluation/) directory for more details.
+
+## Deep Research Agent Family
+
+<p align="center">
+  <img width="100%" src="https://github.com/Alibaba-NLP/DeepResearch/raw/main/assets/family.png">
+</p>
+
+Tongyi DeepResearch also has an extensive deep research agent family. You can find more information in the following paper:
+
+[1] [WebWalker: Benchmarking LLMs in Web Traversal](https://arxiv.org/pdf/2501.07572)<br>
+[2] [WebDancer: Towards Autonomous Information Seeking Agency](https://arxiv.org/pdf/2505.22648)<br>
+[3] [WebSailor: Navigating Super-human Reasoning for Web Agent](https://arxiv.org/pdf/2507.02592)<br>
+[4] [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://arxiv.org/pdf/2507.15061)<br>
+[5] [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://arxiv.org/pdf/2508.05748)<br>
+[6] [WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents](https://arxiv.org/pdf/2509.13309)<br>
+[7] [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://arxiv.org/pdf/2509.13313)<br>
+[8] [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://arxiv.org/pdf/2509.13312)<br>
+[9] [WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning](https://arxiv.org/pdf/2509.13305)<br>
+[10] [Scaling Agents via Continual Pre-training](https://arxiv.org/pdf/2509.13310)<br>
+[11] [Towards General Agentic Intelligence via Environment Scaling](https://arxiv.org/pdf/2509.13311)
+
+
+## 🌟 Misc
+
+<div align="center">
+
+[![Star History Chart](https://api.star-history.com/svg?repos=Alibaba-NLP/DeepResearch&type=Date)](https://www.star-history.com/#Alibaba-NLP/DeepResearch&Date)
+
+</div>
+
+## 🚩 Talent Recruitment
+
+🔥🔥🔥 We are hiring! Research intern positions are open (based in Hangzhou、Beijing、Shanghai)
+
+📚 **Research Area**：Web Agent, Search Agent, Agent RL, MultiAgent RL, Agentic RAG
+
+☎️ **Contact**：[yongjiang.jy@alibaba-inc.com]()
+
+## Contact Information
+
+For communications, please contact Yong Jiang (yongjiang.jy@alibaba-inc.com).
+
+## Citation
 ```bibtex
+@misc{webweaver2025,
+      title={WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research},
+      author={Tongyi DeepResearch Team},
+      year={2025},
+      eprint={2509.13312},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://huggingface.co/papers/2509.13312}
+}
+
 @misc{tongyidr,
   author={Tongyi DeepResearch Team},
   title={Tongyi-DeepResearch},