FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks
- URL: http://arxiv.org/abs/2608.22842v1
- Date: Mon, 24 Aug 2026 06:20:18 GMT
- Title: FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks
- Abstract summary: FinixDoc is an end-to-end agentic parsing system for real-world financial documents.<n>We present FinixDocVL, a 4Bscale vision model built on Qwen3-VL-4B as its core.<n>For evaluation, we construct FinixDocBench, a financial-domain evaluation suite covering digital-native, camera-captured, ultra-large-page, and internal-workflow scenarios.
- Score: 35.14324618292104
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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, as its core parser. To characterize the gap between benchmark and deployment performance, we introduce a Document Parsing Capability Matrix organized along two practical axes: visual quality and document scale. Guided by this matrix, FinixDoc-VL is trained with a domain-adapted recipe combining homoglyph-aware contrastive learning and multi-stage reinforcement learning with composite domain-specific rewards. To better leverage our accumulated advantage in low-quality financial-document data and support large-scale, high-quality data production, we further build a human-in-the-loop Data Factory pipeline with confidence-aware expert review. For evaluation, we construct FinixDocBench, a financial-domain evaluation suite covering digital-native, camera-captured, ultra-large-page, and internal-workflow scenarios, with a compliance-reviewed subset released alongside this technical report. On its main subsets, FinixDoc-VL achieves the highest overall score (81.43) among evaluated baselines, outperforming the next-best open-source model by 5.13 points, with the largest gains on internal financial workflows (FinixInner: 84.08 vs. 78.73).
Related papers
- Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies [0.0]
We compare four multi-agent orchestration architectures for extracting structured information from financial documents.<n>We find that reflexive architectures achieve the highest field-level F1 (0.943) but at 2.3x the cost of sequential baselines.<n>We present ablation studies on semantic caching, model routing and adaptive retry strategies, demonstrating that hybrid configurations can recover 89% of the reflexive architecture's accuracy gains at only 1.15x baseline cost.
arXiv Detail & Related papers (2026-03-24T00:02:47Z) - Agentar-Fin-OCR [6.212573253529528]
Agentar-Fin-OCR is a document parsing system tailored to financial-domain documents.<n>To address finance-specific challenges such as complex layouts, cross-page structural discontinuities, Agentar-Fin-OCR combines a Cross-page Contents Consolidation algorithm.<n>Experiments demonstrate that our model shows high performance on the table parsing metrics of OmniDocBench.
arXiv Detail & Related papers (2026-03-11T17:59:42Z) - FinForge: Semi-Synthetic Financial Benchmark Generation [4.3298251304921775]
FinForge is a scalable, semi-synthetic pipeline for constructing finance-specific evaluation benchmarks.<n>We produce FinForge-5k, a snapshot benchmark comprising over 5,000 human-validated question-answer pairs across 11 finance.<n>FinForge-5k reveals significant differences in financial reasoning, with leading models achieving accuracy levels near 80%.
arXiv Detail & Related papers (2026-01-11T01:38:33Z) - Enhancing Business Analytics through Hybrid Summarization of Financial Reports [0.152292571922932]
Financial reports and earnings communications contain large volumes of structured and semi structured information.<n>We present a hybrid summarization framework that combines extractive and abstractive techniques to produce concise and factually reliable summaries.<n>These findings support the development of practical summarization systems for distilling lengthy financial texts into usable business insights.
arXiv Detail & Related papers (2025-12-28T16:25:12Z) - FinSight: Towards Real-World Financial Deep Research [68.31086471310773]
FinSight is a novel framework for producing high-quality, multimodal financial reports.<n>To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism.<n>A two-stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports.
arXiv Detail & Related papers (2025-10-19T14:05:35Z) - Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
arXiv Detail & Related papers (2025-10-17T02:33:16Z) - DocReward: A Document Reward Model for Structuring and Stylizing [107.03974018371058]
DocReward is a document reward model that evaluates documents based on their structure and style.<n>It is trained using the Bradley-Terry loss to score documents, penalizing predictions that contradict the annotated ranking.<n>It achieves a significantly higher win rate of 60.8%, compared to GPT-5's 37.7% win rate.
arXiv Detail & Related papers (2025-10-13T13:36:32Z) - FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering [57.18367828883773]
FinAgentBench is a benchmark for evaluating agentic retrieval with multi-step reasoning in finance.<n>The benchmark consists of 26K expert-annotated examples on S&P-500 listed firms.<n>We evaluate a suite of state-of-the-art models and demonstrate how targeted fine-tuning can significantly improve agentic retrieval performance.
arXiv Detail & Related papers (2025-08-07T22:15:22Z) - Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding [88.88844606781987]
Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks.<n>The way they model and exploit the interactions between vision and language on documents has hindered them from better generalization ability and higher accuracy.<n>In this work, we investigate the problem of vision-language joint representation learning for VrDU mainly from the perspective of supervisory signals.
arXiv Detail & Related papers (2022-06-27T09:58:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.