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LAVA: Logic-Aware Validation and Augmentation Frame... | AI Research

Key Takeaways

  • LAVA (Logic-Aware Validation and Augmentation) is a modular framework designed to automate the auditing of complex financial documents, such as tax forms, ba...
  • 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.
  • LAVA supports robust rule grounding, fine-grained error attribution, and consistent, traceable end-to-end execution, capabilities essential for high-stakes deployment.
  • LAVA (Logic-Aware Validation and Augmentation) is a modular framework designed to automate the auditing of complex financial documents, such as tax forms, bank statements, and loan applications.
Paper AbstractExpand

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-rule retrieval, layout-preserving information extraction, auxiliary metadata enrichment, and auditable symbolic/arithmetic verification. LAVA supports robust rule grounding, fine-grained error attribution, and consistent, traceable end-to-end execution, capabilities essential for high-stakes deployment. Evaluated on a large real-world benchmark with diverse financial documents and dozens of expert-curated validation rules, LAVA outperforms baselines in hallucination control and edge-case handling while maintaining efficient token usage, demonstrating practicality for high-volume, time-critical validation.

LAVA (Logic-Aware Validation and Augmentation) is a modular framework designed to automate the auditing of complex financial documents, such as tax forms, bank statements, and loan applications. The system aims to replace manual or rigid rule-based pipelines with a multimodal approach that ensures accuracy, consistency, and auditability when verifying documents against business rules.

How the Framework Works

LAVA operates through a four-stage pipeline that processes documents and business rules in parallel:

  1. Retrieval: The system uses a bidirectional constraint mechanism to match specific documents with relevant business rules. It uses lightweight classifiers and semantic encoders to filter out irrelevant data, ensuring the model only processes necessary information. 2. Extraction: Documents are converted into a structured, HTML-like markup that preserves layout, visual groupings, and field dependencies. This process includes noise reduction to remove headers, footers, and boilerplate text, which helps the model focus on critical content. 3. Augmentation: The system injects metadata—such as document type, language, and domain-specific terms—into the reasoning process. This provides the model with necessary context to interpret ambiguous fields. 4. Verification: LAVA splits tasks into two paths. An "Arithmetic Processor" generates formulas for numerical tasks and executes them using an external Python interpreter to avoid calculation errors. A "Symbolic Reasoner" handles semantic and contextual logic directly.

Ensuring Auditability and Accuracy

To address the common issue of model hallucinations, LAVA incorporates a fallback auditing loop for arithmetic tasks. If a secondary "checker" model detects a mismatch between the rule and the calculated result, the system regenerates the formula using the previous error as a negative example. The framework also uses a template-based prompt construction system rather than relying on few-shot examples, which the authors note can be brittle when applied to the diverse layouts found in financial documents.

Performance and Evaluation

The authors evaluated LAVA using a real-world benchmark consisting of approximately 1,000 scanned documents from a proprietary Canadian mortgage database. The evaluation focused on the system's ability to handle diverse layouts and complex logical dependencies. According to the paper, LAVA outperformed baseline models in controlling factual hallucinations and managing numerical infidelity. The authors report that the modular design reduces computational overhead compared to standard multimodal large language model pipelines, as it allows for targeted updates and independent debugging of individual modules.

Considerations

The framework is designed to be backbone-agnostic, meaning it can be integrated with various multimodal large language models. However, the authors note that the system relies on the quality of upstream document analysis tools, such as OCR and layout parsing services. While the modular design is intended to improve maintainability, the effectiveness of the validation depends on the accuracy of the initial document-to-rule matching and the precision of the metadata extracted during the augmentation phase.

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