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 introduce \textsc{InvestLogicBench}, a process-native benchmark containing 201,247 documented decisions from 151 real-world investors. Each episode instantiates a \textbf{P$\rightarrow$E$\rightarrow$R...
| Authors | Yuanhong Jiang, Jingjie Zou, Rui Jiang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 August 2026 |
| Last updated at source | 20 August 2026 |
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