Improve model card: Add metadata, paper, and code links

README.md

@@ -1,11 +1,38 @@
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- safety
+- tool-use
+- guardrail
+- agents
 ---
 
-TS-Guard is a guardrail model for step-level tool invocation safety detection. TS-Guard is trained via reinforcement learning with a multi-task reward scheme tailored for agent security, enabling identifying harmful user requests and attack vectors in agent-environment interaction logs, detecting unsafe tool invocation before execution, and providing interpretable analysis and reasoning process
+# TS-Guard
 
+TS-Guard is a guardrail model for step-level tool invocation safety detection, introduced in the paper [ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback](https://huggingface.co/papers/2601.10156).
+
+TS-Guard is trained via reinforcement learning with a multi-task reward scheme tailored for agent security, enabling identifying harmful user requests and attack vectors in agent-environment interaction logs, detecting unsafe tool invocation before execution, and providing interpretable analysis and reasoning process.
+
+## Resources
+- **Paper:** [ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback](https://huggingface.co/papers/2601.10156)
+- **Repository:** [GitHub - MurrayTom/ToolSafe](https://github.com/MurrayTom/ToolSafe)
 
 ![image](https://cdn-uploads.huggingface.co/production/uploads/66632a3d2dc4dff9a98c38a5/WglXmL5O1Se7L5KuA7T-3.png)
 
 
 ![image](https://cdn-uploads.huggingface.co/production/uploads/66632a3d2dc4dff9a98c38a5/AL3z3FcEFwYCyFmWJI9k5.png)
+
+## Citation
+
+If you find our work helpful, please consider citing it:
+
+```bibtex
+@article{mou2026toolsafe,
+  title={ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback},
+  author={Mou, Yutao and Xue, Zhangchi and Li, Lijun and Liu, Peiyang and Zhang, Shikun and Ye, Wei and Shao, Jing},
+  journal={arXiv preprint arXiv:2601.10156},
+  year={2026}
+}
+```