ReThinker: Scientific Reasoning by Rethinking with Guided Reflection and Confidence Control
Revolutionizing Expert-Level AI Reasoning with Adaptive Intelligence
ReThinker introduces an innovative framework for expert-level scientific reasoning, significantly outperforming existing models on challenging benchmarks like Humanity's Last Exam (HLE). Its core strength lies in adaptive computation allocation, guided multi-dimensional reflection, and robust confidence-weighted selection, fostering a new paradigm in AI problem-solving.
Key Metrics & Impact
ReThinker's novel approach delivers tangible performance gains across critical scientific reasoning benchmarks.
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HLE Benchmark Accuracy
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GAIA Benchmark Accuracy
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XBench-DeepSearch Accuracy
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Improvement over Gemini-3-Pro (HLE)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
ReThinking
Guided Reflection
Confidence Control
ReThinker's rethinking capability involves iteratively questioning and refining intermediate conclusions, moving beyond single-pass reasoning. This iterative process, supported by multi-round synthesis in the Solver stage, allows for progressive refinement of solutions, significantly improving accuracy by systematically addressing errors early in the reasoning trajectory.
The guided reflection mechanism in ReThinker provides structured, dimension-specific error diagnosis. Unlike conventional reflection methods that often miss subtle errors due to context length limitations, ReThinker's Critic stage processes complete prior trajectories, summarizing key steps, final answers, and areas for improvement. This enables comprehensive analysis of both fine-grained issues and high-level logical flaws, leading to more effective error correction.
ReThinker's confidence control system employs explicit uncertainty quantification and multi-round adjudication to stabilize answer selection. Using perplexity-based internal consistency metrics and Latin Square permutations, the Selector dynamically allocates computation, prioritizing uncertain cases. This robust selection process mitigates ordering bias and sampling variance, ensuring optimal answer identification and high-fidelity supervision signals.
52.2%
HLE Benchmark Accuracy (Gemini-3-Pro)
ReThinker's Stage-wise Reasoning Process
Solver Stage: Iterative Synthesis
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Critic Stage: Guided Reflection
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Selector Stage: Confidence-Weighted Selection
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Final Answer Adjudication
| Method | HLE Score (%) |
|---|---|
| ReThinker (Gemini-3-Pro) | 52.2 (SOTA)
|
| Gemini-3-Pro (standalone) | 38.3
|
| GPT-5-high (standalone) | 35.2
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| MiroThinker-v1.0 (30B) | 33.4
|
| ReThinker (OpenPangu-72B) | 33.1
|
Adaptive Tool Orchestration in Action
ReThinker dynamically allocates computation, enabling adaptive tool invocation and guided multi-dimensional reflection. This leads to efficient resource utilization and robust reasoning across complex tasks. For instance, in real-world scenarios, it consistently achieves higher accuracy by judiciously applying web search and code execution tools only when necessary, avoiding unnecessary computation and improving overall efficiency. The Solver phase uses tools heavily for initial exploration, while the Critic and Selector phases significantly reduce tool usage, focusing on refinement and selection based on synthesized internal representations.
Advanced ROI Calculator
Estimate the potential return on investment for integrating ReThinker's capabilities into your enterprise operations.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your Implementation Roadmap
A phased approach to integrate ReThinker into your existing scientific reasoning and data analysis workflows.
Phase 01: Discovery & Strategy
Conduct a deep dive into existing reasoning processes, identify high-impact use cases, and define clear success metrics. Develop a tailored integration strategy for ReThinker.
Phase 02: Pilot & Customization
Implement ReThinker on a selected pilot project. Fine-tune data synthesis pipelines and reflection mechanisms with domain-specific knowledge. Validate initial performance gains.
Phase 03: Scalable Rollout & Optimization
Expand ReThinker across target departments. Continuously monitor performance, refine confidence control parameters, and integrate new tools as needed for sustained high-accuracy reasoning.
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