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Sync your thinking with AI reasoning models to achieve deeper cognitive alignment Follow, learn, and iterate the thought within one turn

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! **

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

• 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

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

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