README.md

metadata

title: Enterprise Active Reading Framework
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit

Enterprise Active Reading Framework Demo

A demonstration of the Active Reading concept from "Learning Facts at Scale with Active Reading" adapted for enterprise document processing.

What is Active Reading?

Active Reading is a breakthrough approach where AI models generate their own learning strategies to study documents, achieving significant improvements in fact learning and retention:

• 66% accuracy on SimpleQA (+313% relative improvement)
• 26% accuracy on FinanceBench (+160% relative improvement)

Demo Features

This Hugging Face Space demonstrates:

• Self-Generated Learning Strategies: The model creates its own approach to reading documents
• Multiple Analysis Types: Fact extraction, summarization, question generation
• Domain Detection: Automatically identifies document type (Finance, Legal, Technical, Medical)
• Interactive Interface: Try different strategies on various document types

Enterprise Applications

The full framework supports:

• 📊 Financial report analysis
• ⚖️ Legal document review
• 🔧 Technical documentation processing
• 🏥 Medical research summarization
• 🏢 General business document analysis

How to Use

1. Select a sample document or paste your own text
2. Choose an Active Reading strategy
3. Click "Apply Active Reading" to see the AI's analysis
4. Explore the extracted facts, generated questions, and summaries

Technical Implementation

This demo uses:

• Transformer Models: For natural language understanding
• Pattern Recognition: For fact extraction and domain detection
• Self-Supervised Learning: Models generate their own training tasks
• Gradio Interface: For interactive demonstration

Full Enterprise Version

This is a simplified demo. The complete Enterprise Active Reading Framework includes:

• Multi-format Support: PDF, Word, databases, APIs
• Enterprise Security: PII detection, encryption, audit logging
• Scalable Deployment: Docker, Kubernetes, monitoring
• Advanced Evaluation: Custom benchmarks and performance metrics

For the full implementation, visit: GitHub Repository

Citation

Based on the research paper:

Lin, J., Berges, V.P., Chen, X., Yih, W.T., Ghosh, G., & Oğuz, B. (2024). 
Learning Facts at Scale with Active Reading. arXiv:2508.09494.