Financial Reporting and XBRL

Neueste Paper

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

  Wenqing Wang, Haitao Xiang, Xinyi Zhao, Mingming Yin, Ying Zhong, Zhaoxin Huan, Qiheng Zhou, Jin Zhu, Xiaolu Zhang, Shi Chang, Jun Zhou · 23. September 2026

  Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, authoritative source selection, entity and period alignment, unit and definition consistency, and verifiable evidence for all conclusions. Existing b…

• Data-Centric Post-Training for Financial Reasoning: Mining, Distillation, and Verifiable Learning

  Zhirayr Hayrapetyan, Andrei Kalmykov, Denis Kokosinskii, Dmitry Stanishevskii, Dmitry Zmitrovich · 10. September 2026

  Financial text, textbooks, and question-answer pairs are abundant, but only a small fraction is directly usable for reasoning-focused post-training. Existing QA pairs often lack explicit reasoning, sufficient context, or reliably verifiable answers, while textbooks must first be transformed into syn…

• IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA

  Yuwen Chiu (Georgia Institute of Technology) · 9. September 2026

  We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC filings and multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. Our central observation is that the 344 developme…

• When Financial Fine-tuning Fails: A Three-Level Detectability Analysis of Numerical Hallucination in Domain-Adapted Language Models

  Xiaodong Li, Peiwei Liu · 7. September 2026

  Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant practical risks. While prior work often attributes such hallucination to insufficient numerical reasoning, this assumption has not been systematical…

• Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements

  Arianna Miola, Bruno Spaccavento, Lorenzo Silotto, Marco Bianchetti, Luca Cagliero · 4. September 2026

  The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity, substantial length, technical language, and inhomogeneity of both textual and numerical content across different jurisdictions and institutions. We i…

• FinLifeBench: Exhaustive Life-Event History and Financial-State Reconstruction from Longitudinal Banking Dialogue

  Hangyeul Lee, Juyoung Oh, Jaeyong Ko, Sunmin Kim, Jaeik Park, Hyunkyu Kim, Jungmin Son, Pilsung Kang · 2. September 2026

  Repeated banking interactions require assistants to maintain complete, current, and traceable customer records as life changes emerge incidentally in routine requests. Existing benchmarks emphasize question answering, bounded episodes, or targeted recall rather than exhaustive longitudinal reconstru…

• AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation

  Yajing Yang, Yunshan Ma, Kelvin J. L. Koa, Min-Yen Kan · 2. September 2026

  We argue that financial report generation should operate at the analytical rather than structural level, composing content from data-derived insights rather than high-level topics or sections. To this end, we propose AnalysisBank, which distills expert reports into a reusable library of Analyses, ea…

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

  Yan Lin, Jingyu Sun, Zhongliang Guo, Qing Li, Zhuohan Xie, Yuxia Wang · 31. August 2026

  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…

• VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings

  Menghan Liu, Elynn Chen · 31. August 2026

  Across audit applications, judgments must be supported by reasonable evidence. However, standard financial language models prioritize fluency over evidence. They are built for general financial reasoning and may produce plausible but ambiguous answers, creating a grounding gap that makes them unsuit…

• From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning

  Lokendra Birla, Milind Savagaonkar, Visnu Srinivasan, Sowmya Rasipuram, Shubhashis Sengupta · 31. August 2026

  Financial question answering (QA) has emerged as a key benchmark for evaluating the performance of Large Language Models (LLMs) on domain-specific tasks involving complex data formats such as tables, charts, and rich textual narratives. While recent advancements have enabled models to reason across …

• Towards Expert Financial QA via Self-Improving RAG

  Junjie Xiong, Shawheen Ghezavat, Aum Hirpara · 28. August 2026

  Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goal with Self-Improving RAG, a framework that decomposes docume…

• CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering

  Kunjesh Parekh, Anil Kumar Tiwari, Divya Saxena · 28. August 2026

  Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plaus…

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

  Suyang Zhong, Jingzhe Zhu, Qi Xu, Liyao Sun, Yin Wang, Qingqing Sun, Shuai Chen, Tianyi Zhang · 27. August 2026

  Deploying large language models for professional financial review requires more than measuring general financial competence: models must perform the specific review operation required by a workflow and determine whether available evidence is sufficient for a defensible decision. Existing financial b…

• FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks

  Hang Wang, Jin Zhang, Guoliang Xu, Pengyue Lu, Yao Li, Zijiao Zhang, Tianyu Huang, Weiqi Xiong, Yulong Wang, Chuqiao Lu, Wenkang Huang, Kai Yang, Yadong Li, Hui Li, Xingzhong Xu, Xiao Xu · 25. August 2026

  Financial document parsing requires accuracy, structural consistency, and verifiability that current benchmarks often fail to reflect. We present FinixDoc, an end-to-end agentic parsing system for real-world financial documents, with FinixDoc-VL, a 4B-scale vision-language model built on Qwen3-VL-4B…

• Reliable Financial Named Entity Recognition under Domain Shift

  Zihao Zheng, Baichuan Li, Junyi Yao, Jiayu Long · 21. August 2026

  Financial AI systems often train information extractors on one textual register and deploy them across filings, news, and user-generated content, while standard F1 scores do not indicate which predictions remain safe to automate when the input distribution changes. We study confidence estimation and…

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

  Pratik Ghawate · 20. August 2026

  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, allo…

• LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing

  Ruoqi Shu, Xuhui Wang, Isaac Wang, Yanming Mai, Bo Wan · 18. August 2026

  Financial document validation in production, such as payroll auditing, tax compliance, and loan underwriting, demands exceptional accuracy, consistency, and reproducibility under strict enterprise constraints. In practice, documents arrive with heterogeneous layouts and formats, semantically rich an…

• Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents

  Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Shuguang Ma, Fei Yu, Yanghua Xiao, Zhixu Li · 14. August 2026

  Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a c…

• V-FiLLM: Verified Financial LLM Reasoning Benchmark

  Alicia Larsen, Victoire Laurent, Aulia Kharis Rakhamsari, Lara Turgut, Nino Antulov-Fantulin · 12. August 2026

  While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains comparatively less explored. We introduce V-FiLLM, a framework that generates financial reasoning benchmarks from executable computation trees grounded in…

• Can Open-Weight Models Compete on Financial Text Comprehension?

  Jan Sp\"orer · 11. August 2026

  Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months. Yet their reliability on real-world financial tasks remains largely untested. We updated the Financial Touchstone benchmark, which now has 2,967 question context-answer …

• FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings

  Sasan Mansouri, Daniel Saad, Mark Wahrenburg, Manu Weissel, Fabian Woebbeking · 10. August 2026

  Financial question answering is typically evaluated by answer correctness, yet in SEC filings a plausible and even numerically correct answer can be grounded in the wrong evidence. Similar facts and disclosures recur across sections of a filing, across reporting periods of the same firm, and across …

• FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation

  Yinghao Tang, Tan Zhenwei, Yiyao Wang, Wanli Gu, Xiaolu Zhang, Jun Zhou, Wei Chen · 6. August 2026

  Large language models can produce fluent financial analysis, but fluency alone does not establish whether a report is suitable for institutional delivery. We introduce FinReportBench, an expert-grounded benchmark for measuring and improving institution-grade financial report generation. Expert revie…

• FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables

  Ben Wang, Kang Zhou, Lifan Guo, Feng Chen, Chi Zhang · 6. August 2026

  Evaluating financial AI agents requires criteria aligned with real professional work. Existing rubric methods typically derive criteria from task prompts or model outputs, overlooking tacit standards visible only in practitioner deliverables. We introduce FinProBench, a benchmark for professional fi…

• Benchmarks Are Not Validation: A System-Level View of Financial LLM Applications

  Burak Payzun, İrem Demirtaş, Simona Scala, Elena Ferretti, Seçil Arslan · 3. August 2026

  Large language models are increasingly deployed in financial applications that combine retrieval, proprietary data, tool use, orchestration logic, monitoring, and human escalation. Yet evaluation often remains model-centric: benchmark scores, task accuracy, or one-off qualitative reviews are treated…

• APEX-Accounting

  Julien Benchek, Austin Bennett, Jasmin Kern, Ryan Stevens, Rene Sultan, Charis Ching, Hayley Popiel, Vaibhav Mittal, Felix Mercier, Brendan Foody, Bertie Vidgen · 30. Juli 2026

  We introduce APEX-Accounting, a benchmark built by Mercor in partnership with Ramp, to assess whether frontier models can do the real work of accountants. Tasks include reconciling accounts, accruing expenses, posting transactions, and producing reports. The private eval set comprises 160 tasks, spl…