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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 benchmark for evaluating the investment logic of large language models (LLMs). The benchmark measures the logical plausibility and event grounding of LLMs, suggesting that outcome-only evaluation hides weakly grounded reasoning. The authors argue for a data-system interface with versioned profiles, temporal provenance, inspectable retrieval, decision ledgers, and replayable outcomes.

What the Document Says

  • The paper presents InvestLogicBench, a benchmark consisting of 201,247 investment decisions from 151 real-world investors. The benchmark evaluates the logical plausibility and event grounding of LLMs.
  • Across four leading LLMs, logical plausibility remains near 4/5, while event grounding is only 0.8--2.8/5.
  • The authors argue that outcome-only evaluation hides weakly grounded reasoning.
  • They propose that a data-system interface for personalized, consequential agents should include versioned profiles, temporal provenance, inspectable retrieval, decision ledgers, and replayable outcomes.

Key Concepts Mentioned

  • InvestLogicBench
  • large language models (LLMs)
  • investment logic
  • logical plausibility
  • event grounding

Claims Made by the Document

  • Outcome-only evaluation hides weakly grounded reasoning.
  • A data-system interface for personalized, consequential agents should include versioned profiles, temporal provenance, inspectable retrieval, decision ledgers, and replayable outcomes.

Evidence Used

  • The paper presents a benchmark that measures logical plausibility and event grounding of LLMs across four leading models.
  • The authors state that outcome-only evaluation hides weakly grounded reasoning and propose the data-system interface to address this issue.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.