miromind-ai/MiroThinker-v1.0-72B
MiroThinker-v1.0-72B by miromind-ai is a 72.7 billion parameter open-source research agent based on Qwen2.5-72B-Instruct, designed for advanced tool-augmented reasoning and information-seeking. It features a 256K context window and can handle up to 600 tool calls per task, significantly improving interactive scaling by training the model to manage deeper and more frequent agent-environment interactions. This model excels in general research performance across various benchmarks, demonstrating predictable performance improvements with increased interaction depth.
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MiroThinker-v1.0-72B: Advanced Tool-Augmented Research Agent
MiroThinker v1.0-72B, developed by miromind-ai, is a 72.7 billion parameter open-source research agent built upon the Qwen2.5-72B-Instruct base model. It is specifically engineered to enhance tool-augmented reasoning and information-seeking capabilities through a novel concept called interactive scaling. Unlike traditional methods that focus solely on model size or context length, MiroThinker systematically trains the model to handle deeper and more frequent interactions with its environment, leveraging feedback to correct errors and refine its approach.
Key Capabilities
- Interactive Scaling: Achieves significant performance gains by increasing the frequency and depth of tool-augmented interactions, leading to 8-10 points accuracy improvements on benchmarks.
- Extended Context Window: Supports a 256K context window for long-horizon reasoning and multi-step analysis.
- High Tool Call Capacity: Capable of handling up to 600 tool calls per task, a substantial improvement over previous open-source agents.
- Strong General Research Performance: Demonstrates robust performance across benchmarks like HLE-Text (37.7%), BrowseComp (47.1%), BrowseComp-ZH (55.6%), and GAIA-Text-103 (81.9%), narrowing the gap with commercial counterparts.
When to Use This Model
MiroThinker-v1.0-72B is ideal for research and development in agentic AI, particularly for tasks requiring:
- Complex Problem Solving: Where multi-step reasoning and iterative refinement through tool use are critical.
- Information Acquisition: Tasks that benefit from extensive web searching and information extraction.
- Deep Interaction with Environments: Use cases demanding frequent and deep engagement with external tools and feedback loops.
This model is suitable for developers and researchers looking to push the boundaries of agentic intelligence and explore advanced tool-use paradigms.