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README.md

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-title: README
+title: Proactive Interactive Reasoning (PIR)
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 short_description: Enables reasoning-LLM to ask clarification questions
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-Edit this `README.md` markdown file to author your organization card.
+# Reasoning While Asking: Transforming Reasoning LLMs into Proactive Inquirers (PIR)
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.22139-b31b1b.svg)](https://arxiv.org/abs/2601.22139)
+[![GitHub](https://img.shields.io/badge/GitHub-Proactive--Interactive--R1-black?logo=github)](https://github.com/Proactive-Interactive-R1)
+[![SwanLab](https://img.shields.io/badge/SwanLab-Training%20Logs-438440)](https://swanlab.cn/@chenx/Proactive-Interactive-R1)
+
+This organization hosts the official models and datasets for the paper **"Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers"**.
+
+## 💡 Motivation
+
+Current reasoning LLMs (e.g., GPT-o1, DeepSeek-R1) suffer from **blind self-thinking**: they perform extensive internal reasoning even when critical information is missing or user intent is ambiguous. This leads to overthinking, hallucinations, and misaligned conclusions.
+
+**PIR (Proactive Interactive Reasoning)** is a new paradigm that transforms reasoning LLMs from passive solvers into **proactive inquirers**. Instead of guessing, PIR-enabled models detect uncertainty during reasoning and actively ask users for clarification before proceeding.
+
+![PIR Framework Overview](https://raw.githubusercontent.com/Proactive-Interactive-R1/Proactive-Interactive-R1/main/image/paradigm.png)
+*(Note: If the image above does not load, please view it on our [GitHub](https://github.com/Proactive-Interactive-R1))*
+
+### Key Features
+
+- **User-Intent Alignment**: Optimizes interaction through US-GRPO with composite rewards balancing accuracy, efficiency, and helpfulness.
+- **Significant Improvements**: Up to **32.70% higher accuracy**, **22.90% higher pass rate**, and **41.36 BLEU improvement** over baselines.
+- **Reduced Computation**: Nearly halves unnecessary reasoning tokens and interaction turns.
+
+## 📦 Models
+
+We provide the following models trained with the PIR paradigm:
+
+| Model Name | Description | Link |
+| :--- | :--- | :--- |
+| **Proactive-Interactive-R1-Math-7B** | The core model optimized for mathematical reasoning with clarification capabilities. | [View Model](https://huggingface.co/Proactive-Interactive-R1/Proactive-Interactive-R1-Math-7B) |
+| **Proactive-Interactive-R1-Math-7B-Pro** | An enhanced version of the Math-7B model. | [View Model](https://huggingface.co/Proactive-Interactive-R1/Proactive-Interactive-R1-Math-7B-Pro) |
+| **Proactive-Interactive-R1-SFT-7B** | The base SFT model before Reinforcement Learning alignment. | [View Model](https://huggingface.co/Proactive-Interactive-R1/Proactive-Interactive-R1-SFT-7B) |
+
+## 📚 Datasets
+
+The datasets used to train and evaluate PIR are available here:
+
+- **[Reasoning-While-Asking-SFT-Dataset](https://huggingface.co/datasets/Proactive-Interactive-R1/Reasoning-While-Asking-SFT-Dataset)**: The dataset used for the initial Supervised Fine-Tuning (SFT) phase.
+- **[DeepSeek-R1-Distill-Data-5k](https://huggingface.co/datasets/Proactive-Interactive-R1/DeepSeek-R1-Distill-Data-5k)**: Distilled data used for training.
+
+## 🔬 Method
+
+PIR consists of two phases:
+
+1.  **Interactive Capability Activation (Phase I)**:
+    *   Detects uncertainty via **Predictive Entropy** at each reasoning step.
+    *   Injects clarification questions at high-uncertainty points using instruction-following LLMs.
+    *   Performs **Supervised Fine-Tuning** to teach models the "think-ask-respond" pattern.
+
+2.  **User-Intent Alignment (Phase II)**:
+    *   **US-GRPO**: Group Relative Policy Optimization with a dynamic User Simulator.
+    *   **Composite Reward**: Combines output accuracy (extrinsic) with reasoning efficiency and helpfulness (intrinsic).
+    *   Aligns model behavior with user intent while minimizing unnecessary interactions.
+
+## 📜 Citation
+
+If you find this work useful, please cite our paper:
+
+```bibtex
+@misc{chen2026reasoningaskingtransformingreasoning,
+      title={Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers}, 
+      author={Xin Chen and Feng Jiang and Yiqian Zhang and Hardy Chen and Shuo Yan and Wenya Xie and Min Yang and Shujian Huang},
+      year={2026},
+      eprint={2601.22139},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.22139},
+}