Finance

Abstract

Recent analysis of U.S. fiscal dynamics suggests a structural constraint emerging around the end of this decade, driven by rising interest burdens relative to government revenue.

This paper explores the possibility that a system-level reset may already be underway, not as a discrete event, but as a gradual process of currency adjustment, inflation, and asset repricing.

A modeled 20–40% devaluation of the U.S. dollar (~30% midpoint) materially alters debt sustainability trajectories, extending the fiscal runway by an estimated 10–20 years.

Executive Summary

This post applies a simple, testable framework to Ireland’s fiscal system:

Fiscal constraint emerges when the cost of debt rises relative to the revenue supporting it.

In large, stable systems like the United States, this dynamic unfolds gradually. Ireland presents a different case.

While headline metrics suggest strength, three structural factors create a distinct risk profile:

1. Revenue composition: A significant portion derives from multinational activity and is not fully under domestic control.
2. Measurement distortion: The effective economic base (GNI*) is ~43% smaller than GDP implies.
3. Debt repricing: Existing debt is being refinanced at materially higher interest rates.

These factors introduce a critical refinement to the model:

How We Used AI to Analyze When U.S. Debt Becomes a Constraint

Executive Summary

We demonstrate a human + AI research process.

AI was used to:

• refine the question
• identify the correct metric
• expose assumptions
• and test the model through adversarial critique

The goal was to transform a vague macro concern into a quantifiable, testable system model.

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The Question

We began with a simple but vague concern:

“Is U.S. debt becoming a problem?”

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: Real-Time Self-Tuning with MR.Q

Most machine learning models need hundreds of examples, large GPUs, and hours of training to learn anything useful. But what if you could build a system that gets smarter with just a handful of preference examples, runs entirely on your CPU, and improves while you work?

That’s exactly what MR.Q offers.

🔍 It doesn’t require full retraining.
⚙️ It doesn’t modify the base model.
🧠 It simply learns how to judge quality — and does it fast.

Summary

Forecasting future events is a critical task in fields like finance, politics, and technology. However, improving the forecasting abilities of large language models (LLMs) often requires extensive human supervision. In this post, we explore a novel approach from the paper LLMs Can Teach Themselves to Better Predict the Future that enables LLMs to teach themselves better forecasting skills using self-play and Direct Preference Optimization (DPO). We’ll walk through a Python implementation of this method, step by step.

Summary

Large Language Models (LLMs) offer immense potential, but realizing that potential often requires fine-tuning them on task-specific data. This guide provides a comprehensive overview of LLM fine-tuning, focusing on practical implementation with LLaMA-Factory and the powerful LoRA technique.

What is Fine-Tuning?

Fine-tuning adapts a pre-trained model to a new, specific task or dataset. It leverages the general knowledge already learned by the model from a massive dataset (source domain) and refines it with a smaller, more specialized dataset (target domain). This approach saves time, resources, and data while often achieving superior performance.