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