Large Language Models (LLMs) use persistent user context such as memory, profiles, and role prompts. This enables personalization, but can destabilize evidence-based judgment: the same evidence may yield different conclusions under different user contexts. Finance offers a high-stakes long-document setting where evidence reading is central. We test this on 3,575 SEC filings across twelve models. By comparing persona-conditioned retrieval, neutral retrieval, and memory-framed context, we find that bias comes from how models interpret identical evidence under role-style context, not from which evidence they retrieve. As mitigation, user-memory framing of the same mindset and dual-output scoring reduce the effect, with model-specific effectiveness across models.