LLMQuant/quant-mind — reverse-engineered prompt

Build me a Python framework called QuantMind for quant finance research. I want to give it an arXiv paper id, or a plain English request, and have it fetch the paper, parse the useful content, tag the topic, extract structured research knowledge, and return clean JSON that another app could use.

Please make the main experience simple for a researcher, with one function for a single paper and another for running a batch with progress and error handling. It should support configurable LLM models through environment variables, include a clear example script, and have enough tests that I can trust the core flow works.

Keep the design modular so more sources like news, blogs, reports, and SEC filings can be added later, and so embeddings or RAG style retrieval can plug in later. Use uv for setup, write practical documentation, and include a short quick start that lets me run one arXiv example end to end.