Intelligent-Internet/CoT-Lab

CoT-Lab: Human-AI Co-Thinking Laboratory

Huggingface Spaces 🤗 | GitHub Repository 🌐 中文README

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. 1.Set Initial Prompt Describe your prompy in the input box (e.g., "Explain quantum computing basics")

1. 1.Adjust Cognitive Parameters
2. 2.⏱ Thought Sync Throughput: tokens/sec - 5:Read-aloud, 10:Follow-along, 50:Skim
3. 3.📏 Human Thinking Cadence: Auto-pause every X paragraphs (Default off - recommended for active learning)

1. 1.Interactive Workflow
2. 2.Click Generate to start co-thinking, follow the thinking process
3. 3.Edit AI's reasoning when it pauses - or pause it anytime with Shift+Enter
4. 4.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 or OpenAI SDK compatible API.

# 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)