Tracing Positional Bias in Financial Decision-Making: Mechanistic Insights from Qwen2.5

The growing adoption of large language models (LLMs) in finance exposes high-stakes decision-making to subtle, underexamined positional biases. The complexity and opacity of modern model architectures compound this risk. We present the first unified framework and benchmark that not only detects and quantifies positional bias in binary financial decisions but also pinpoints its mechanistic origins within open-source Qwen2.5-instruct models (1.5B-14B). Our empirical analysis covers a novel, financ...