EDGAR

🕒 TL;DR We explore MARS, a multi-agent prompt optimizer using Socratic dialogue. We implement it using DSPy + Fin-R1 + EDGAR giving us an end-to-end financial reasoning pipeline. We deploy the whole thing to Hugging Face Spaces with a Gradio UI. 🌟 Introduction Prompt engineering has become the defining skill of the Large Language Model (LLM) era a delicate balance between science and art. Crafting the perfect prompt often feels like an exercise in intuition, trial, and error.

Introduction Fin-R1 is a new model specifically fine-tuned for financial reasoning, with performance that beats much larger models like DeepSeek-R1. This post will use this model and compare it with phi3 across various tasks. phi3 for comparison Phi-3: a lightweight, general-purpose model known for its efficiency and strong reasoning performance at smaller parameter scales. It serves as a great baseline for assessing how domain-specific tuning in Fin-R1 improves financial understanding and response structure.