MiroMind

MiroThinker: The General Purpose Solver for Verifiable Accuracy

In the evolving landscape of artificial intelligence, the transition from probabilistic generation to verifiable accuracy marks a new era of technology. MiroThinker stands at the forefront of this shift as a General Purpose Solver designed for critical tasks. Built by MiroMind, a global AI frontier lab, MiroThinker is not just a conversation partner; it is a Reasoning OS engineered to provide 300 steps to 99% certainty.

What's MiroThinker?

MiroThinker is a high-capacity, 235B parameter model specifically built for deep reasoning rather than mere pattern matching. Unlike standard Large Language Models (LLMs) that rely on rapid pattern recognition, MiroThinker utilizes System 2 Thinking—a deliberate, structured logical deduction process that verifies every step before proceeding.

At its core, MiroThinker operates under the MiroMind OS, an operating system for reasoning that manages state, memory, and policy execution. This allows the system to move beyond simple text generation into the realm of complex problem-solving, achieving a new standard for trust in AI-driven decision-making.

Key Features of MiroThinker

1. Verification-Centric Architecture

MiroThinker is built from the ground up for reasoning and self-evolution. The architecture is divided into specialized roles:

• Planner: Designs the reasoning chain as a step_graph (DAG), defining nodes with unique IDs and state hashes.
• Executor: Carries out the specific logical steps.
• ChainChecker & Verifier: Ensure that every node in the reasoning chain meets the required threshold of reliability (>99.99% per-step).

2. DAG Reasoning Protocol

The system employs a Directed Acyclic Graph (DAG) reasoning protocol which includes:

• Branch: Parallel exploration of alternative solutions.
• Rollback: The ability to return to specific steps while retaining confirmed facts if an error is detected.
• Replan: Rewriting subgraphs in real-time when new evidence breaks the current plan.

3. High-Confidence Performance

With 300 steps to 99% certainty, MiroThinker outperforms competitors in stability and accuracy. Benchmark data shows MiroThinker leading in complex tasks such as GAIA, HLE, and BrowseComp, specifically optimized for long-chain reasoning that standard models cannot handle.

4. Self-Evolution and Continuous Learning

MiroThinker is a system that improves itself. Through internal SWE-bench testing and "self-surgery" capabilities, it learns from every problem solved. While currently a leader in code domains, it is actively migrating its expertise to math, science, and finance logic domains.

Use Cases for MiroThinker

Manufacturing and System Design

MiroThinker is applied to failure analysis and complex system design, where a single logical error can lead to significant physical consequences.

Finance and Risk Modeling

In the financial sector, the General Purpose Solver is used for risk modeling, compliance checks, and algorithmic trading. Its traceable reasoning chains provide full auditability for institutional investors, as seen in deployments like Menos AI.

Legal and Compliance

MiroThinker handles contract analysis and regulatory mapping with 99% certainty, ensuring that critical legal interpretations are both accurate and verifiable.

Science and Bio-Pharma

From hypothesis generation to drug discovery and molecular simulations, MiroThinker accelerates scientific breakthroughs by ensuring the underlying experimental design is logically sound.

Software Engineering

As a primary driver for autonomous code generation and architecture optimization, the "slow but right" approach of MiroThinker ensures that the generated code is production-ready and debugged through rigorous reasoning.

How to Use the MiroThinker Reasoning OS

MiroThinker utilizes a structured approach to solve complex prompts. For example, when asked about macro-economic impacts on the U.S. Nasdaq Index, the system follows a clear logical path:

1. Macro Analysis: It identifies the biggest movers, such as the NFP Jobs Report, ADP data, and 10-Year Treasury Auctions, analyzing how yields affect tech stocks.
2. Sector-Specific Insights: It evaluates events like CES 2026 or international trade rules (e.g., China's chip equipment rules) to predict volatility in semiconductor stocks.
3. Logical Deduction: Using the DAG Node Structure, it assigns a step_id, parent_step_id, and branch_id to every piece of the analysis.
4. Verification: The ChainChecker verifies the status of each node (verified | failed) before the final output is delivered to the user.

FAQ

Q: What makes MiroThinker different from GPT or other LLMs? A: While standard LLMs focus on conversation and rapid pattern matching, MiroThinker is a Reasoning OS designed for System 2 Thinking. It uses a 235B parameter model to execute 300-step reasoning chains with 99% cumulative accuracy.

Q: What is a DAG Reasoning Protocol? A: It is a structured method where reasoning is mapped as a Directed Acyclic Graph. This allows the AI to branch into parallel alternatives, rollback to previous steps if an error is found, and replan sub-tasks without losing overall progress.

Q: Is MiroThinker suitable for production environments? A: Yes. MiroThinker is already deployed in production environments for asset management (Menos AI), health management (Theta Health), and enterprise operations (Tanka).

Q: Who is the team behind MiroThinker? A: The team consists of over 80 members, with more than 70% holding PhDs from top global institutions and 100% having experience at top-tier tech firms like Alibaba, Huawei, and Tencent.

Q: Where is MiroMind headquartered? A: MiroMind is headquartered in Redwood City, CA, with a co-R&D and operational hub in Singapore.