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README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+<h1 align="center">
+🛡️ MemPrivacy-1.7B-RL
+</h1>
+
+<p align="center">
+  <div style="display: flex; justify-content: center; gap: 10px;">
+    <a href="https://github.com/MemTensor/MemPrivacy">
+      <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub"/>
+    </a>
+    <a href="https://huggingface.co/IAAR-Shanghai/MemPrivacy-1.7B-RL">
+      <img src="https://img.shields.io/badge/🤗%20Hugging%20Face-MemPrivacy--1.7B--RL-yellow" alt="Hugging Face"/>
+    </a>
+    <a href="https://arxiv.org/">
+      <img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv" alt="Paper"/>
+    </a>
+  </div>
+</p>
+
+MemPrivacy-1.7B-RL is a lightweight, privacy-preserving model developed from the Qwen3-1.7B base model and further optimized through reinforcement learning. It is designed specifically for personalized memory management in edge-cloud agents, enabling more reliable, adaptive, and privacy-aware memory operations.
+
+This model functions as the core local extraction engine within the **MemPrivacy framework**. Instead of relying on aggressive masking that destroys task-relevant semantics, the model accurately identifies privacy-sensitive spans on edge devices, categorizes them according to a four-level privacy taxonomy, and replaces them with semantically structured, type-aware placeholders (e.g., `<Email_1>`) before transmitting data to the cloud. The original values are securely stored locally and restored when the cloud agent returns a response.
+
+---
+
+## ✨ Key Features & Capabilities
+
+* **High-Precision Privacy Extraction**: Achieves state-of-the-art performance in privacy information extraction, substantially surpassing strong general-purpose reasoning models like GPT-5.2 and Gemini-3.1-Pro.
+* **Four-Level Privacy Taxonomy (PL1-PL4)**: Capable of identifying and classifying privacy-relevant content based on identifiability, expected harm, and operational exploitability, enabling fine-grained, user-configurable protection policies.
+* **Semantic Utility Preservation**: By decoupling privacy protection from semantic destruction, the use of typed placeholders ensures that cloud agents retain the relational and semantic cues required for effective memory formation, retrieval, and personalization. 
+* **Edge-Optimized Efficiency**: Designed for resource-constrained local deployment, maintaining high accuracy while significantly reducing inference latency compared to massive general-purpose LLMs.
+
+---
+
+## 🚀 Usage Example
+
+The model accepts conversational text alongside basic user identifiers and extracts a structured list of privacy instances, detailing the original text, the specific privacy type, and its corresponding privacy level.
+
+**Input:**
+
+```text
+User Name: Zhang San
+Dialogue Text: Hello, my name is Zhang San, and my mobile number is 13800138000. I've been having insomnia recently, and the doctor diagnosed me with mild depression. Here is a photo of my prescription. Also, I just received a verification code 89757, please fill it in for me. By the way, I like spicy food and I speak quite directly.
+```
+
+**Output:**
+
+JSON
+
+```
+[
+  {
+    "original_text": "Zhang San",
+    "privacy_type": "Real Name",
+    "privacy_level": "PL2"
+  },
+  {
+    "original_text": "13800138000",
+    "privacy_type": "Phone Number",
+    "privacy_level": "PL2"
+  },
+  {
+    "original_text": "mild depression",
+    "privacy_type": "Medical Health",
+    "privacy_level": "PL3"
+  },
+  {
+    "original_text": "89757",
+    "privacy_type": "Verification Code",
+    "privacy_level": "PL4"
+  }
+]
+```
+
+------
+
+## 📚 Citation
+
+```
+
+```