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The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalization can affect evidence-based judgment: the same evidence may lead to different conclusions under different user contexts. Finance provides a high-stakes setting to study this problem because decisions often depend on interpreting long and complex documents. We test this using 3,575 SEC filings across twelve LLMs. We compare persona-conditioned retrieval, neutral retrieval, and memory-framed context to separate the effect of evidence selection f

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