Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals, horizons, portfolios, and risk boundaries. Yet financial LLMs are evaluated either by static question answering or by terminal profit and loss. The former omits agency; the latter cannot reveal whether a profitable action was grounded, profile-consistent, or merely lucky. We ask whether the community is using the wrong ruler for consequential agents. We int
Record details
Published: 6 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy · Finance, VC & PE
Retrieved: 7 August 2026
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ethics.ai (6 August 2026), “Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents,” evidence record 17351, https://ethics.ai/record/17351 (originally published by arXiv cs.AI).
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