Cross-Modal Cognitive Mapping

A Technical Overview of System Design and Implementation

Author: Ernan Hughes
Published: April 2025

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Abstract

Cross-Modal Cognitive Mapping is a new framework designed to extend traditional text-based cognition modeling into multimodal representations.
This system combines text prompts, visual generation, human selection behavior, and semantic memory retrieval to better understand and track human conceptual architectures.

This post presents a technical overview of the core architecture, database design, embedding workflows, search functionality, and resonance mapping built during the initial research phase.

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?

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.

Introduction

In the rapidly evolving world of artificial intelligence, reinforcement learning (RL) stands out as a powerful framework for training AI agents to make decisions in complex and dynamic environments. However, traditional RL algorithms often come with a significant drawback: they are highly specialized and require meticulous tuning for each specific task, making them less adaptable and more resource-intensive.

Enter MR.Q (Model-based Representations for Q-learning)—a groundbreaking advancement in the field of reinforcement learning. MR.Q bridges the gap between model-based and model-free methods by achieving the high performance typically associated with model-based approaches while retaining the simplicity and efficiency of model-free algorithms. The secret behind MR.Q’s success lies in its ability to learn model-based representations without the need for costly trajectory simulations, setting a new standard for adaptability and efficiency in AI-driven decision-making.

Summary

Machine learning operates on data. Essentially, it processes data to extract meaningful information, which can then be used to make intelligent decisions. This is the foundation of Artificial Intelligence. The more data you have the better your machine learning apps will be. There is a caveat though the data has to be high quality. The more data you have and the higher quality the better your apps will be.

Summary

Artificial Intelligence (AI) has revolutionized the way we generate and consume text. From chatbots crafting customer responses to AI-authored articles, artificial intelligence is reshaping how we create and consume content. As AI-generated text becomes indistinguishable from human writing, distinguishing between the two has never been more critical. Here are some of the reasons it is important to be able to verify the source of information:

• Preventing plagiarism
• Maintaining academic integrity
• Ensuring transparency in content creation
• If AI models are repeatedly trained on AI-generated text, their quality may degrade over time.

In this blog post, we’ll explore the current most effective methods for detecting AI-generated text.

Summary

Could the greatest playwright of all time have secretly shaped one of the most influential religious texts in history? Some believe William Shakespeare left his mark on the King James Bible hidden in plain sight. With the power of AI, we’ll investigate whether there’s any truth to this conspiracy.

You can read about the conspiracy here:

• Did Shakespeare write the King James Bible?

• Shakespeare and the King James Bible – Some Tentative Conclusions

Summary

Vector databases are essential for modern AI applications like semantic search, recommendation systems, and natural language processing. They allow us to store and query high-dimensional vectors efficiently. With the pgvector extension PostgreSQL becomes a powerful vector database, enabling you to combine traditional relational data with vector-based operations.

In this post, we will walk through the full process:

Installing PostgreSQL and pgvector Setting up a vector-enabled database Generating embeddings using Ollama Running similarity queries with Python By the end, you’ll be able to store, query, and compare high-dimensional vectors in PostgreSQL, opening up new possibilities for AI-powered applications.

Summary

The evolution of technology is driven by protocols structured ways for systems to communicate and interact. The internet, APIs, and even modern databases rely on protocols to function efficiently. Similarly, as AI becomes more powerful, it needs a structured and standardized way to manage context across interactions.

Enter the Model Context Protocol (MCP) a framework designed to enhance the way AI models understand, retain, and utilize context over multiple exchanges. Large Language Models (LLMs) are powerful, but without effective context management, they can: