We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends the agent with heavy-duty reasoning capabilities for more reliable multi-step problem solving. In particular, MiroThinker-1.7 improves the reliability of each interaction step through an agentic mid-training stage that emphasizes structured planning, contextual reasoning, and tool interaction. This enables more effective multi-step interaction and sustained reasoning across complex tasks. MiroThinker-H1 further incorporates verification directly into the reasoning process at both local and global levels. Intermediate reasoning decisions can be evaluated and refined during inference, while the overall reasoning trajectory is audited to ensure that final answers are supported by coherent chains of evidence. Across benchmarks covering open-web research, scientific reasoning, and financial analysis, MiroThinker-H1 achieves state-of-the-art performance on deep research tasks while maintaining strong results on specialized domains. We also release MiroThinker-1.7 and MiroThinker-1.7-mini as open-source models, providing competitive research-agent capabilities with significantly improved efficiency.

翻译：我们提出了MiroThinker-1.7，这是一种专为复杂长程推理任务设计的新型研究智能体。在此基础上，我们进一步介绍了MiroThinker-H1，它通过增强重型推理能力，实现了更可靠的多步骤问题求解。具体而言，MiroThinker-1.7通过强调结构化规划、情境推理和工具交互的智能体中期训练阶段，提升了每个交互步骤的可靠性。这使得在复杂任务中能进行更有效的多步骤交互和持续推理。MiroThinker-H1进一步将验证机制直接整合到局部和全局层面的推理过程中。中间推理决策可在推断过程中进行评估和优化，同时对整体推理轨迹进行审计，以确保最终答案得到连贯证据链的支持。在涵盖开放网络研究、科学推理和金融分析的基准测试中，MiroThinker-H1在深度研究任务上实现了最先进的性能，同时在专业领域保持了强劲的表现。我们还开源了MiroThinker-1.7和MiroThinker-1.7-mini模型，以显著提升的效率提供了具有竞争力的研究智能体能力。