Skip to content

FinExam-10K: When Retrieval Helps Financial Reasoning?

Aug 2026 · 0 citations · 36 references
Computer Science

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).

View source

Similar papers

#natural language process... Preprint Sep 2026

FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability

FinFIRST is the first financial benchmark to jointly evaluate answers and supporting evidence through atomic rubrics, retaining final-answer correctness as the primary objective while making the supporting research process measurable, verifiable, and diagnosable.

Wen-Qing Wang, Hai-Tao Xiang, Xin-Yi Zhao et al. · 0 citations
Review Aug 2026

FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review

FinRiskAtlas is introduced, a Chinese-language benchmark that evaluates financial LLMs along two complementary dimensions: operation execution under fixed evidence states and evidence-state control under evolving review conditions, and shows that broad financial capability scores do not fully capture where models are r...

Su-Yang Zhong, Jingzhe Zhu, Qi Xu et al. · 1 citation
Preprint Aug 2026

FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents

Evaluating FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow, finds that the tool harness, not the model alone, strongly shapes quality and efficiency.

Yuhao Zhang, O. Koyluoglu, Thejas Venkatesh et al. · 3 citations · ⚡2
#artificial intelligence Preprint Aug 2026

FinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Systems

Large language models are increasingly used to support financial operations, but their apparent reasoning performance can depend on whether they receive the right evidence. In financial reconciliation, the evidence needed for diagnosis is distributed across invoices, purchase orders, approvals, allocations, payments, l...

Prof. S. B. Ghawate · 2 citations

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

MIT News · Artificial Intelligence Sep 24, 2026

Estimating suicide risk from text

A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.