Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

MiroMind Team, S. Bai, L. Bing, L. Lei, R. Li, X. Li, X. Lin, E. Min, L. Su, B. Wang, L. Wang, L. Wang, S. Wang, X. Wang, Y. Zhang, Z. Zhang, G. Chen, L. Chen, Z. Cheng, Y. Deng, Z. Huang, D. Ng, J. Ni, Q. Ren, X. Tang, B. L. Wang, H. Wang, N. Wang, C. Wei, Q. Wu, J. Xia, Y. Xiao, H. Xu, X. Xu, C. Xue, Z. Yang, Z. Yang, F. Ye, H. Ye, J. Yu, C. Zhang, W. Zhang, H. Zhao, P. Zhu

Abstract

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.

MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

Abstract

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.
Paper Structure (55 sections, 13 equations, 7 figures, 4 tables)

This paper contains 55 sections, 13 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Agentic Large Language Models
  4. Deep Research Agents
  5. Agentic Workflow
  6. Formulation
  7. Step Loop
  8. Episode Loop
  9. Tools
  10. Information Retrieval
  11. Code Execution
  12. File and Data Transfer
  13. Implementation Details
  14. Sliding-Window Filtering
  15. Result Truncation
  16. ...and 40 more sections

Figures (7)

  • Figure 1: Comparison of MiroThinker with state-of-the-art agents and agentic foundation models.
  • Figure 2: The overview of MiroThinker-1.7 & H1.
  • Figure 3: Overview of the dual-pipeline QA synthesis framework. The Corpus-based Pipeline (left) focuses on topical breadth and high-throughput generation from document subgraphs. The WebHop Pipeline (right) constructs calibrated reasoning trees with web-augmented expansion and hierarchical verification to ensure reasoning rigour and controllable complexity.
  • Figure 4: The agentic training pipeline of MiroThinker-1.7.
  • Figure 5: Training dynamics of MiroThinker-1.7-mini for GRPO Agentic RL. BrowseComp-200 is our selected challenging subset from BrowseComp for faster evaluation during training. The plotted curves represent a running average with a window size of 5 to highlight the optimization trends.
  • ...and 2 more figures