Fine-Tuning

The financial industry has always prided itself on cold precision. For decades, quantitative models and spreadsheets dominated boardrooms and trading desks. But that orthodoxy is now under siege. Not from another statistical breakthrough, but from something surprisingly human-like: Large Language Models (LLMs). Recent research shows a dramatic shift in how AI—particularly LLMs like GPT-4 and LLaMA—is being integrated across financial workflows. Far from just summarizing news or answering earnings call questions, LLMs are now organizing entire investment pipelines, fine-tuning themselves on proprietary data, and even collaborating as autonomous financial agents. A recent survey by Mahdavi et al. (2025) categorized over 70 state-of-the-art systems into four distinct architectural frameworks, offering us a lens through which to assess the future of financial AI. ...

General-purpose language models can solve math puzzles and explain Kant, but struggle to identify a ticker or classify earnings tone. What the financial world needs isn’t more reasoning—it’s better reading. Over the past year, large language models (LLMs) have surged into every corner of applied AI, and finance is no exception. But while the promise of “reasoning engines” captivates headlines, the pain point for financial tasks is much simpler—and more niche. ...

The next generation of quantitative investment agents must be more than data-driven—they must be logic-aware and structurally adaptive. Two recently published research efforts provide important insights into how reasoning patterns and evolving workflows can be integrated to create intelligent, verticalized financial agents. Kimina-Prover explores how reinforcement learning can embed formal reasoning capabilities within a language model for theorem proving. Learning to Be a Doctor shows how workflows can evolve dynamically based on diagnostic feedback, creating adaptable multi-agent frameworks. While each stems from distinct domains—formal logic and medical diagnostics—their approaches are deeply relevant to two classic quant strategies: the Black-Litterman portfolio optimizer and a sentiment/technical-driven Bitcoin perpetual futures trader. ...