arXiv — NLP / Computation & Language · · 3 min read

FinExam-10K: When Retrieval Helps Financial Reasoning?

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Computer Science > Computation and Language

arXiv:2608.28155 (cs)
[Submitted on 28 Aug 2026]

Title:FinExam-10K: When Retrieval Helps Financial Reasoning?

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Abstract:Professional financial examinations require models to combine domain knowledge, calculation, and judgment, yet no benchmark covers the full CFA and FRM structure under one protocol. We introduce FinExam-10K, to our knowledge the largest reported English benchmark for this setting, with 10,198 expert-reannotated questions spanning CFA Levels I-III and FRM Parts I-II. We release 5,110 questions and sequester 5,088 for a quarterly maintained leaderboard. To separate coverage from local answerability, we report a 10,198-item Full-Coverage Track and a 7,625-item Context-Complete Reasoning Track, which is the primary basis for claims about reasoning from the supplied record. Across 17 models, the best accuracy is 85.29% overall. On the frozen Hard band, the best score is 34.68% on the Full-Coverage Track and 54.57% on the 372 context-complete items. All 17 models share 47 context-complete failures. Function-RAG and FunctionGraph-RAG rescue hundreds of errors but also overturn many correct answers, producing little or negative net gain. A gate trained only on public data decides from the question and initial response when FunctionGraph-RAG should run. On the 5,088 held-out items, the gate invokes FunctionGraph-RAG for 7.9% of questions and improves accuracy from 70.83% to 71.23% (p = .0446).
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.28155 [cs.CL]
  (or arXiv:2608.28155v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.28155
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yan Lin [view email]
[v1] Fri, 28 Aug 2026 10:16:14 UTC (2,403 KB)
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