A better way to read scientific papers.
Simple Papers transforms complex academic papers into digestible, simplified content with AI-powered explanations, interactive annotations, and audio narration.
Check out the app here!
Or watch this video demo here.
- Attention Is All You Need
- ReAct: Synergizing Reasoning and Acting in Language Models
- XGBoost: A Scalable Tree Boosting System
- Latent Dirichlet Allocation
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- High-Resolution Image Synthesis with Latent Diffusion Models
- A Unified Approach to Interpreting Model Predictions
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- A Practical Guide for Evaluating LLMs and LLM-Reliant Systems
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- AWS Bedrock - AI foundation models
- LangChain - Framework for building AI applications
- agentic-doc - Document parsing and extraction
- ElevenLabs - AI voice synthesis for audio narration
- Streamlit - Web application framework
Simple Papers can be used in two ways:
🌐 Online Mode (Streamlit Community Cloud)
- Access pre-parsed and simplified academic papers
- No setup required - just click and read
- Perfect for exploring research without the complexity
💻 Local/Dev Mode
- Parse and simplify your own PDF papers
- Full control over processing and customization
- Ideal for researchers working with specific documents
- Note: Currently a proof-of-concept that requires multiple API keys for the end-to-end workflow. This complexity will be streamlined if the app gains sufficient user engagement and traction.
git clone https://github.com/syasini/simple_papers.git
cd simple_papers
pip install -e .Run the Streamlit app:
streamlit run app.pyThen open your browser to http://localhost:8501 and start simplifying papers!
- Python 3.12+
- Streamlit
- API keys for local processing (see
secrets.toml.examplefor setup details) - AWS credentials (for advanced features)
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License.