MARS

✨ Summary

Imagine an AI that gets smarter every time it works not by retraining on massive datasets, but by learning from its own reasoning and reflection, just like humans.

Most AI systems are frozen in time. Trained once, deployed forever, they never learn from mistakes or build on successes. Real intelligence human or artificial doesn’t work that way. It learns from experience.

This is the vision behind Stephanie: a self-improving AI that gets better every time it acts, not by fine-tuning, but by remembering, reusing, and revising its reasoning.

Summary

Large Language Models (LLMs) like DeepSeek-R1 show remarkable reasoning abilities, but how these abilities are internally represented has remained a mystery. This paper explores the mechanistic interpretability of reasoning in LLMs using Sparse Autoencoders (SAEs) — a tool that decomposes LLM activations into human-interpretable features. In this post, we’ll:

• Explain the SAE architecture used • Compute and visualize ReasonScore • Explore feature steering with sample completions • Provide live visualizations using Python + Streamlit

🕒 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. But what if we could take the guesswork out of the process? What if prompts could optimize themselves?