# DeepWism-R2 **Repository Path**: mirrors_trending/DeepWism-R2 ## Basic Information - **Project Name**: DeepWism-R2 - **Description**: DeepWism R2 is a next-generation AGI system built on the T3CEDS framework (Thin-Thick-Thin Crowd Entropy Dynamics System), which redefines intelligence as a process of entropy reduction rather than attention modeling. - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-07-09 - **Last Updated**: 2026-02-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
DeepWism® R2
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## 1. Introduction We introduce DeepWism® R2(Research&Report), a revolutionary next-generation AI system based on the agents framework - a Thin-Thick-Thin Crowd Entropy Dynamics System (T3CEDS) that establishes entropy reduction as the fundamental mechanism underlying crowd intelligence. This represents a paradigm shift in AI research, moving from traditional attention-based mechanisms to entropy management as the core design principle. DeepWism® R2 innovative architecture consists of three distinct yet interconnected layers: **Thin Perception Layer:** Efficiently captures high-dimensional inputs while preserving essential information and reducing input entropy **Thick Processing Layer:** Leverages crowd intelligence mechanisms to actively reduce entropy through structured reasoning, collaborative processing, and deep ranking **Thin Decision Layer:** Distills complex representations into coherent outputs, further reducing entropy to facilitate clear, actionable decisions DeepWism® R2 achieves **27.5%** accuracy on Humanity's Last Exam (HLE), surpassing OpenAI DeepResearch's **26.6%** and establishing new state-of-the-art performance. In addition, it leads on xbench-ScienceQA with **70.0%** and xbench-DeepSearch with **64.0%**, outperforming all other models. These results demonstrate superior capabilities in: Complex Problem-Solving: Enhanced reasoning through entropy reduction mechanisms Uncertainty Management: Effective handling of high-entropy scenarios Multi-Domain Generalization: Robust performance across diverse problem domains including science, deep retrieval, and logic AI Explainability: Transparent decision-making processes through entropy dynamics

## 2. Summary **Revolutionary Architecture: Thin-Thick-Thin Crowd Entropy Dynamics System** - Entropy-Centric Design: Unlike traditional attention-based models, DeepWism® R2 is fundamentally designed around entropy reduction principles. This paradigm shift enables more effective handling of complex, uncertain problem spaces that challenge conventional AI systems. - Crowd Intelligence Integration: The thick processing layer implements sophisticated crowd intelligence mechanisms that leverage collective reasoning patterns to systematically reduce entropy through collaborative processing and structured analysis. - T3CEDS Framework: The three-layer architecture optimizes information flow from high-entropy inputs to low-entropy, actionable outputs, ensuring maximum coherence and decision clarity at each stage. **Performance Excellence: State-of-the-Art Results** - Humanity's Last Exam Leadership: Achieving 27.5% accuracy on HLE, DeepWism® R2 sets new benchmarks in complex reasoning tasks, demonstrating superior performance over leading models including OpenAI DeepResearch, Gemini2.5, and Claude 3.7 Sonnet. - Multi-Benchmark Dominance: Consistent excellence across diverse evaluation metrics including AIME2025 (93.3%), GPQA-Diamond (88.0%), MMLU (86.0%), and SWE-bench Verified (72.0%), showcasing robust generalization capabilities. - Open Research Commitment: DeepWism® R2 framework will be fully open-sourced with global API access for authenticated users, fostering collaborative advancement in entropy-based AI research. ## 3. Evaluation Results Comprehensive Benchmark Performance DeepWism® R2 demonstrates exceptional performance across multiple challenging benchmarks, establishing new state-of-the-art results in complex reasoning and problem-solving tasks. | **Model** | **xbench-ScienceQA** | **xbench-DeepSearch** | **HLE** | **AIME2025** | **GPQA-Diamond** | **MMLU** | **SWE-bench Verified** | | ------------------- | -------------------- | --------------------- | -------- | ------------ | ---------------- | -------- | ---------------------- | | **DeepWism® R2** | **70.0** | **64.0** | **27.5** | **93.3** | **88.0** | **86.0** | **72.0** | | o3-high | 60.8 | 65.0 | 20.3 | – | – | – | – | | o3-pro | 59.6 | – | – | – | – | – | – | | Doubao-Seed-1.6 | 56.6 | 50.0 | – | – | – | – | – | | OpenAI DeepResearch | – | – | 26.6 | – | – | – | – | | Gemini 2.5 Pro | 59.4 | 50.0 | 21.6 | 88.0 | 86.4 | 84.5 | 67.2 | | o4 mini | 50.4 | 60.0 | 18.1 | 92.7 | 81.4 | 82.0 | 68.1 | | Claude 4 Opus | – | – | 10.7 | 90.0 | 83.3 | 80.7 | 79.4 | | DeepSeek-R1-0528 | 54.6 | – | 14.0 | 87.5 | 81.0 | 84.0 | 57.6 | ### 🔑 Key Performance Highlights * 🏆 **Humanity's Last Exam (HLE): 27.5%** accuracy, surpassing all existing models including OpenAI DeepResearch * 🧪 **xbench-ScienceQA: 70.0%** accuracy, leading all models on complex scientific question answering * 🔍 **xbench-DeepSearch: 64.0%** accuracy, demonstrating top-tier deep retrieval and reasoning capabilities * 📊 **AIME2025**: **93.3%** accuracy, showcasing exceptional mathematical reasoning capabilities * 🔬 **GPQA-Diamond**: **88.0%** accuracy, indicating superior performance in graduate-level science questions * 📚 **MMLU**: **86.0%** accuracy, reflecting strong multi-domain knowledge understanding * 💻 **SWE-bench Verified**: **72.0%** accuracy, proving highly effective software engineering problem-solving skills ## 4. Chat Website Experience DeepWism® R2's revolutionary capabilities through our interactive platforms: 🌐 Chat Interface: [i.deepwism.com](https://i.deepwism.com/) - Real-time interaction with DeepWism® R2 - Entropy visualization in reasoning processes - Multi-domain problem-solving capabilities ## 5. Contact For questions, collaborations, or support ，please contact us at: r2@deepwism.com Website: www.deepwism.com GitHub Issues: Report bugs or request features Twitter: @DeepWism

Advancing Next Generation AI through Entropy Reduction and Crowd Intelligence

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