--- 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}, } ```