Improve model card: add metadata, library name, and paper links

Hi! I'm Niels, part of the community science team at Hugging Face.

I noticed that this model card could be improved with additional metadata and links to help users find and use your model more easily. This PR:
- Updates the `pipeline_tag` to `text-generation` (standard for causal LLMs).
- Adds the `library_name: transformers` tag to enable the "Use in Transformers" button.
- Adds links to the research paper and the official GitHub repository.
- Provides a summary of the Proactive Interactive Reasoning (PIR) paradigm.
- Includes the citation for the paper.

Let me know if you have any questions!

README.md

@@ -1,11 +1,52 @@
 ---
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 language:
 - en
 metrics:
 - accuracy
-base_model:
-- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
-pipeline_tag: question-answering
+library_name: transformers
+pipeline_tag: text-generation
 tags:
 - Reasoning-while-asking
----
+---
+
+# Reasoning While Asking: Proactive Interactive Reasoning (PIR)
+
+This repository contains the weights for the model presented in the paper [Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers](https://huggingface.co/papers/2601.22139).
+
+- **Code:** [GitHub Repository](https://github.com/SUAT-AIRI/Proactive-Interactive-R1)
+
+## Introduction
+
+Reasoning-oriented Large Language Models (LLMs) often remain limited by a *blind self-thinking* paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. 
+
+**PIR (Proactive Interactive Reasoning)** is a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers that interleave reasoning with clarification. PIR-enabled models detect uncertainty during reasoning and actively ask users for clarification before proceeding, reducing hallucinations and misaligned conclusions.
+
+## Demo
+
+Experience the "Reasoning While Asking" capability using the interactive script provided in the [official repository](https://github.com/SUAT-AIRI/Proactive-Interactive-R1):
+
+```bash
+python run_demo_interactive_generation.py \
+    --input_file "verl-tool/datasets/mip/gsm8k.json" \
+    --model_url "http://localhost:1136" \
+    --model_name Proactive-Interactive-R1-Math-7B \
+    --output_dir results/
+```
+
+## Citation
+
+If you find this work useful, please cite the 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},
+}
+```