Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain fundamentally limited by a \emph{blind self-thinking} paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We propose Proactive Interactive Reasoning (PIR), a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers that interleave reasoning with clarification. Unlike existing search- or tool-based frameworks that primarily address knowledge uncertainty by querying external environments, PIR targets premise- and intent-level uncertainty through direct interaction with the user. PIR is implemented via two core components: (1) an uncertainty-aware supervised fine-tuning procedure that equips models with interactive reasoning capability, and (2) a user-simulator-based policy optimization framework driven by a composite reward that aligns model behavior with user intent. Extensive experiments on mathematical reasoning, code generation, and document editing demonstrate that PIR consistently outperforms strong baselines, achieving up to 32.70\% higher accuracy, 22.90\% higher pass rate, and 41.36 BLEU improvement, while reducing nearly half of the reasoning computation and unnecessary interaction turns. Further reliability evaluations on factual knowledge, question answering, and missing-premise scenarios confirm the strong generalization and robustness of PIR. Model and code are publicly available at: \href{https://github.com/SUAT-AIRI/Proactive-Interactive-R1}

翻译：面向推理的大语言模型在思维链提示下取得了显著进展，但其根本仍受限于一种"盲目自我思考"范式：即使在关键信息缺失或模糊时，仍会进行大量内部推理。我们提出主动交互推理——一种新型推理范式，将大语言模型从被动求解者转变为主动询问者，使其能够将推理与澄清交替进行。不同于现有主要针对知识不确定性通过查询外部环境来解决的搜索或工具框架，主动交互推理通过直接与用户交互，针对前提和意图层面的不确定性进行求解。主动交互推理通过两个核心组件实现：（1）一种不确定性感知的监督微调方法，赋予模型交互推理能力；（2）一种基于用户模拟器的策略优化框架，由复合奖励驱动，使模型行为与用户意图对齐。在数学推理、代码生成和文档编辑上的大量实验表明，主动交互推理持续优于强基线方法，准确率提升高达32.70%，通过率提升22.90%，BLEU提升41.36分，同时计算推理成本和冗余交互轮次减少近一半。在事实知识、问答和缺失前提场景上的进一步可靠性评估确认了主动交互推理的强泛化性和鲁棒性。模型和代码已公开在：\href{https://github.com/SUAT-AIRI/Proactive-Interactive-R1}