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AI Agent 'Read' Action Scaling and Verification: Methodological Insights from MiroThinker =========================================================================================
AI Agent 'Read' Action Scaling and Verification: Methodological Insights from MiroThinker =========================================================================================  ### 马东锡 NLP
@dongxi_nlp
如果把 Agent 的 Tool-calling 放进 GRUD 框架里看，read 是风险最低的一类动作，所以 scale read 动作对 Deep research 这类 read-heavy agent 来说，是性能提升的一个最直接有效和安全的方向。
这也正符合 MiroThinker 1 / 1.5 所体现的 test-time interaction scaling, 通过更多探索，换取更好深度搜索表现。
MiroThinker-1.7 / H1 更进一步：它在扩展交互的基础上，把 verification 引入，能够真正推动更加有效交互。
对一切 read-heavy agent 而言，这都是一个非常强的方法论参考：真正该 scale 的，是动作数量 + 质量， scale + verification。
MiroThinker 已经逐渐成为我最常用的 research agent app，期待未来的 MiroThinker API + Personal Agent!Show More
#### MiroMindAI
@miromind_ai · 6d ago
🚀 Introducing MiroThinker-1.7 & MiroThinker-H1
Today, we release the latest generation of our research agent family: MiroThinker-1.7 and MiroThinker-H1.
Our goal is simple but ambitious: move beyond LLM chatbots to build heavy-duty, verifiable agents capable of solving real, critical tasks. Rather than merely scaling interaction turns, we focus on scaling effective interactions — improving both reasoning depth and step-level accuracy.
Key highlights:
🧠 Heavy-duty reasoning designed for long-horizon tasks
🔍 Verification-centric architecture with local and global verification
🌐 State-of-the-art performance on BrowseComp / BrowseComp-ZH / GAIA / Seal-0 research benchmarks
📊 Leading results across scientific and financial evaluation tasks
Explore MiroThinker:
Hugging Fhuggingface.co/collections/mi…CCjw4
Gitgithub.com/MiroMindAI/Mir…ofSuJ Show More
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Mar 17, 2026, 7:11 AM View on X
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3,207 Views  马东锡 NLP @dongxi_nlp
One Sentence Summary
This tweet analyzes how placing an Agent's Tool-calling within the GRUD framework reveals that scaling and verifying 'read' actions are crucial for enhancing the performance of read-heavy agents, illustrating this methodology with MiroThinker.
Summary
This tweet provides an in-depth analysis of how AI Agents can enhance performance by optimizing 'read' actions during Tool-calling. The author posits that within the GRUD framework, 'read' actions carry the lowest risk, making their expansion the most direct, effective, and secure approach to boosting performance for read-heavy agents (such as deep research agents). The tweet further highlights that MiroThinker 1/1.5 achieved deeper search capabilities through test-time interaction scaling, while MiroThinker 1.7/H1 advanced this by incorporating a verification mechanism, thereby fostering more effective interactions. The author emphasizes that 'action quantity + quality, i.e., scaling + verification,' represents a robust methodology applicable to all read-heavy agents, expressing appreciation for MiroThinker as a research agent application and anticipation for its future API. The quoted tweet introduces MiroThinker-1.7/H1 as the latest version of their heavy-duty, verifiable agent, underscoring its reasoning capabilities for long-horizon tasks and its verification-centric architecture.
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AI Agent 'Read' Action Scaling and Verification: Methodol... ===============