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Source note2 min read

Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents

Source

arXiv

arxiv.org/abs/2608.06108v1

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Plain-Language Brief

The paper introduces InvestLogicBench, a new benchmark to evaluate the investment logic of large language models (LLMs). The benchmark focuses on personalized financial decision-making and uses real-world data from 151 investors. It measures the logical plausibility and event grounding of LLMs, revealing that while their reasoning may appear strong, it is often weakly grounded. The authors suggest that a new data-system interface for consequential agents should include versioned profiles, temporal provenance, inspectable retrieval, decision ledgers, and replayable outcomes.

What the Document Says

  • The paper introduces InvestLogicBench, a benchmark used to evaluate the investment logic of large language models (LLMs).
  • InvestLogicBench uses real-world data from 151 investors for personalized financial decision-making evaluation.
  • The benchmark measures logical plausibility and event grounding of LLMs.
  • Results show that while the reasoning of LLMs may appear strong, it is often weakly grounded.
  • The authors suggest a new data-system interface for consequential agents that includes versioned profiles, temporal provenance, inspectable retrieval, decision ledgers, and replayable outcomes.

Key Concepts Mentioned

  • Investment logic
  • Large language models (LLMs)
  • InvestLogicBench
  • Personalized financial decision-making
  • Real-world data
  • Logical plausibility
  • Event grounding
  • Data-system interface

Claims Made by the Document

  • The current evaluation methods for financial LLMs are inadequate.
  • InvestLogicBench provides a more comprehensive evaluation method for financial decision-making by large language models.
  • The results show that the reasoning of leading LLMs is often weakly grounded.
  • A new data-system interface is needed for consequential agents to improve their performance and transparency.

Evidence Used

  • The paper describes InvestLogicBench and its focus on personalized financial decision-making evaluation using real-world data from 151 investors.
  • The results of the evaluation across four leading LLMs are presented, showing the performance in logical plausibility and event grounding.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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$\rightarrow$D$\rightarrow$O} trace: investor \textit{Profile}, observable market \textit{Events}, investment \textit{Reasoning}, executable \textit{Decision}, and delayed \textit{Outcome}. The release includes profile construction, point-in-time event binding, structured logic, horizons, outcomes, and post-mortems, and supports comprehension, profile-conditioned generation, and end-to-end replay. Across four leading LLMs, logical plausibility remains near 4/5 while event grounding is only 0.8--2.8/5; return and process quality also disagree. These results expose polished but weakly grounded reasoning that outcome-only evaluation hides. We further argue that P$\rightarrow$E$\rightarrow$R$\rightarrow$D$\rightarrow$O should be a data-system interface, requiring versioned profiles, temporal provenance, inspectable retrieval, decision ledgers, and replayable outcomes. Finance is our stress test for a broader class of personalized, consequential agents.