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--- |
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title: "CoT-Lab: Human-AI Co-Thinking Laboratory" |
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emoji: "๐ค" |
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colorFrom: "blue" |
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colorTo: "gray" |
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sdk: "gradio" |
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python_version: "3.13" |
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sdk_version: "5.13.1" |
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app_file: "app.py" |
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models: |
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- "deepseek-ai/DeepSeek-R1" |
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tags: |
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- "writing-assistant" |
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- "multilingual" |
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license: "mit" |
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--- |
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# CoT-Lab: Human-AI Co-Thinking Laboratory |
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[Huggingface Spaces ๐ค](https://huggingface.co/spaces/Intelligent-Internet/CoT-Lab) | [GitHub Repository ๐](https://github.com/Intelligent-Internet/CoT-Lab-Demo) |
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[ไธญๆREADME](README_zh.md) |
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**Sync your thinking with AI reasoning models to achieve deeper cognitive alignment** |
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Follow, learn, and iterate the thought within one turn |
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## ๐ Introduction |
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CoT-Lab is an experimental interface exploring new paradigms in human-AI collaboration. Based on **Cognitive Load Theory** and **Active Learning** principles, it creates a "**Thought Partner**" relationship by enabling: |
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- ๐ง **Cognitive Synchronization** |
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Slow-paced AI output aligned with human information processing speed |
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- โ๏ธ **Collaborative Thought Weaving** |
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Human active participation in AI's Chain of Thought |
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** This project is part of ongoing exploration. Under active development, discussion and feedback are welcome! ** |
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## ๐ Usage Guide |
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### Basic Operation |
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1. **Set Initial Prompt** |
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Describe your prompy in the input box (e.g., "Explain quantum computing basics") |
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2. **Adjust Cognitive Parameters** |
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- โฑ **Thought Sync Throughput**: tokens/sec - 5:Read-aloud, 10:Follow-along, 50:Skim |
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- ๐ **Human Thinking Cadence**: Auto-pause every X paragraphs (Default off - recommended for active learning) |
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3. **Interactive Workflow** |
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- Click `Generate` to start co-thinking, follow the thinking process |
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- Edit AI's reasoning when it pauses - or pause it anytime with `Shift+Enter` |
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- Use `Shift+Enter` to hand over to AI again |
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## ๐ง Design Philosophy |
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- **Cognitive Load Optimization** |
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Information chunking (Chunking) adapts to working memory limits, serialized information presentation reduces cognitive load from visual searching |
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- **Active Learning Enhancement** |
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Direct manipulation interface promotes deeper cognitive engagement |
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- **Distributed Cognition** |
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Explore hybrid human-AI problem-solving paradiam |
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## ๐ฅ Installation & Deployment |
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Local deployment is (currently) required if you want to work with locally hosted LLMs. |
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Due to degraded performance of official DeepSeek API - We recommend seeking alternative API providers, or use locally hosted distilled-R1 for experiment. |
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**Prerequisites**: Python 3.11+ | Valid [Deepseek API Key](https://platform.deepseek.com/) or OpenAI SDK compatible API. |
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```bash |
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# Clone repository |
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git clone https://github.com/Intelligent-Internet/CoT-Lab-Demo |
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cd CoT-Lab |
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# Install dependencies |
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pip install -r requirements.txt |
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# Configure environment |
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API_KEY=sk-**** |
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API_URL=https://api.deepseek.com/beta |
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API_MODEL=deepseek-reasoner |
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# Launch application |
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python app.py |
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``` |
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## ๐ License |
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MIT License ยฉ 2024 [ii.inc] |
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## Contact |
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[email protected] (Dango233) |
