README.md

---
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
language:
- en
metrics:
- accuracy
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},
}
```