MiroThinker v1.0 is an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities.
Unlike previous agents that scale only model size or context length, MiroThinker introduces interactive scaling at the model level, systematically training the model to handle deeper and more frequent agent–environment interactions as a third dimension of performance improvement. Interactive scaling leverages environment feedback and external information acquisition to correct errors and refine trajectories.
Empirical results demonstrate the effectiveness of this interactive scaling. Performance across several benchmarks improves predictably as the model engages in increasingly deep and frequent interactions with its environment.
image
Key Features
MiroThinker v1.0 supports a 256K context window, long-horizon reasoning, and deep multi-step analysis.
Handles up to 600 tool calls per task — a substantial improvement over previous open-source research agents.
Released in 8B, 30B, and 72B parameter scales, accompanied by a comprehensive suite of tools and workflows to flexibly support diverse research settings and compute budgets.
MiroThinker v1.0 demonstrates strong general-research performance across a broad range of benchmarks, achieving 37.7%, 47.1%, 55.6%, and 81.9% on HLE-Text, BrowseComp, BrowseComp-ZH, and GAIA-Text-103, respectively. These results surpass previous open-source agents and narrow the gap with commercial counterparts such as GPT-5-high.
To prevent potential information leakage (e.g., searching benchmark answers from HuggingFace), access to HuggingFace has been explicitly disabled in these tools.
MiroThinker
Interactive Scaling
image
The RL-tuned MiroThinker-v1.0-30B model exhibits far longer and deeper interaction trajectories than its SFT counterpart across all four major benchmarks. While SFT models often terminate after only a few tool calls, the RL model performs extended multi-turn reasoning, exploring and verifying information before concluding.
This behavioral shift yields 8–10 points accuracy gains, showing a clear link between interaction depth and performance. We refer to this effect as interactive scaling: increasing the frequency and depth of tool-augmented interactions reliably improves research reasoning capability. This forms a third dimension of scaling—alongside model size and context length—defining MiroThinker’s path toward more general agentic intelligence.
Quick Start
Please refer to our GitHub repository for installation instructions, examples, and full documentation:
MiroThinker v1.0 is released under the MIT License.
Contact Us
MiroThinker is developed by the MiroMind AI Team.
If you would like to leave us a message, feel free to get in touch.
In addition to GitHub,
Discord,
WeChat,
and RedNote,
you can also reach us via email at service@miromind.ai.
