meta-llama--llama-cookbook/end-to-end-use-cases/research_paper_analyzer/README.md

Research Paper analyzer with Llama4 Maverick

This leverages Llama 4 Maverick model to retrieve the references of an arXiv paper and ingest all their content for question-answering without using any RAG to store these information.

Features

Leverage Long Context Length

Model	Meta Llama4 Maverick	Meta Llama4 Scout	OpenAI GPT-4.5	Claude Sonnet 3.7
Context Window	1M tokens	10M tokens	128K tokens	1K tokens

Because of the long context length, the analyzer can process all the reference paper content at once, so you can ask questions about the paper without worrying about the context length.

Getting Started

1. Install dependencies:

pip install -r requirements.txt

2. Run the application:

python research_analyzer.py

3. Open the gradio interface on localhost in the browser.

4. Provide a paper url such as https://arxiv.org/abs/2305.11135

5. Press "Ingest", wait for paper to be processed and ask questions about it