title	emoji	colorFrom	colorTo	sdk	python_version	sdk_version	app_file	models	tags	license
CoT-Lab: Human-AI Co-Thinking Laboratory	🤖	blue	gray	gradio	3.13	5.13.1	app.py	deepseek-ai/DeepSeek-R1	writing-assistant multilingual	mit

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. 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 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

[email protected] (Dango233)