arXiv AI
Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows
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The study examines how user context—such as memory, profiles, and role prompts—affects Large Language Models’ (LLMs) financial analysis. Using 3,575 SEC filings and twelve LLMs, the authors distinguish between evidence selection and interpretation, finding that most context spillover arises from differing interpretations under various roles rather than from retrieving different evidence. They evaluate two mitigation strategies—expressing investor mindset as a user profile instead of an assistant role, and separating evidence-based from personalized outputs—both of which reduce but do not eliminate spillover, with effectiveness varying across models.
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The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis
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IPO Finance Agent: Evaluation of LLM Financial Analysts beyond Finance Agent v2, with Automated Rubric Generation -- the Case of the SpaceX (SPCX) IPO
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IPO Finance Agent: Evaluation of LLM Financial Analysts beyond Finance Agent v2, with Automated Rubric Generation -- the Case of the SpaceX (SPCX) IPO
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