MiroThinker-H1通过双重验证提升长程推理可靠性，适合复杂研究任务。
MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification
- 引入中段训练与双重验证机制，增强每步推理的结构化与可追溯性
- 在开放网络、科学推理等任务上达顶尖性能，支持多步链式证据推理
- 开源轻量版模型，效率显著优于同类研究代理
我们提出 MiroThinker-1.7，一种面向复杂长程推理任务的研究代理。在此基础上，进一步推出 MiroThinker-H1，通过强化重载推理能力实现更可靠的多步问题求解。MiroThinker-1.7 通过强调结构化规划、上下文推理与工具交互的代理中段训练阶段，提升了每一步交互的可靠性，从而支持更有效的多步协作和持续推理。MiroThinker-H1 进一步将验证机制嵌入推理过程，在局部与全局层面实现评估与修正：中间决策可在推理过程中被检验与优化，整体推理轨迹亦经审计以确保最终答案由连贯的证据链支撑。在涵盖开放网络研究、科学推理与金融分析的多个基准测试中，MiroThinker-H1 在深度研究任务上达到当前最优表现，同时保持在专业领域中的强劲性能。我们还开源了 MiroThinker-1.7 与 MiroThinker-1.7-mini 模型，提供具备竞争力的研究代理能力且效率显著提升。
原文摘要 · Abstract (English)
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.
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