LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing
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 and context-dependent content, and embedded business rules that current pipelines struggle to process reliably. We introduce LAVA (Logic-Aware Validation and Augmentation), a modular, backbone-agnostic pipeline built on multimodal large language models, that integrates a four-stage design: document-ru
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- PossiblePossibly related (embedding) · 49%Launch HN: Parsewise (YC P25) – Reason Across Documents with an API →
- PossiblePossibly related (embedding) · 48%Built Technologies builds an AI-powered document intelligence solution on AWS to power agents across real estate finance →
- PossiblePossibly related (embedding) · 46%Building an AI loan document intelligence platform — shipped fraud detection, hybrid search, and 3-tier email fallback this week[P] →
- LinkedLinked via arxiv author · 85%Ruoqi Shu →
“LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing”
- LinkedLinked via arxiv author · 85%Xuhui Wang →
“LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing”
- LinkedLinked via arxiv author · 85%Isaac Wang →
“LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing”
- LinkedLinked via arxiv author · 85%Yanming Mai →
“LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing”
- LinkedLinked via arxiv author · 85%Jingbo Wang →
“LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing”