Enterprise AI Analysis
MoDora: Tree-Based Semi-Structured Document Analysis System
Authors: BANGRUI XU, QIHANG YAO, ZIRUI TANG, XUANHE ZHOU, YEYE HE, SHIHAN YU, QIANQIAN XU, BIN WANG, GUOLIANG LI, CONGHUI HE, FAN WU
MoDora is an LLM-powered system designed for semi-structured document analysis, addressing challenges like fragmented OCR elements, lack of hierarchical representations, and scattered information retrieval. It achieves this through local-alignment aggregation, a novel Component-Correlation Tree (CCTree) for hierarchical organization, and a question-type-aware retrieval strategy. Experimental results demonstrate MoDora's significant outperformance against existing baselines, achieving accuracy improvements ranging from 5.97% to 61.07%.
Executive Impact
MoDora brings transformative capabilities to enterprise document analysis, delivering quantifiable improvements in data accuracy and operational efficiency.
61.07%
Accuracy Improvement Over Baselines
91%
Retrieval Recall (LLM-guided selector)
84%
Irrelevant Nodes Filtered (Verifier)
Deep Analysis & Enterprise Applications
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System Overview
MoDora addresses key challenges in semi-structured document analysis by transforming fragmented OCR outputs into coherent, layout-aware components, organizing them hierarchically with a Component-Correlation Tree (CCTree), and employing a sophisticated question-type-aware retrieval mechanism. This integrated approach ensures robust evidence localization and accurate answer generation across diverse document types and query complexities.
Addressing Core Challenges
The system tackles three main technical challenges: (1) fragmented OCR elements losing semantic context, (2) ineffective representation of hierarchical structures and layout distinctions, and (3) difficulties in retrieving and aligning relevant information scattered across documents. MoDora's multi-stage preprocessing, tree construction, and retrieval mechanisms are specifically designed to overcome these limitations.
MoDora's Innovative Methodology
MoDora employs a local-alignment aggregation strategy for component creation, a Component-Correlation Tree (CCTree) for hierarchical modeling with bottom-up summarization, and a question-type-aware retrieval mechanism integrating LLM-guided node selection, embedding-based fallback, and MLLM-based cross-modal verification. This comprehensive framework enables superior performance in semi-structured document analysis.
61.07%
Accuracy Improvement Over Baselines
Enterprise Process Flow
Document Preprocessing (OCR, Aggregation, Extraction)
→
Tree Construction (CCTree, Summarization)
→
Tree-Based Document Analysis (Question Parsing, Retrieval)
→
Answer Generation
| Question Type | GPT-5 | DocAgent | MoDora |
|---|---|---|---|
| Hierarchy | 39.62% | 55.97% | 76.73% |
| Text | 51.35% | 68.47% | 79.95% |
| Hybrid | 39.20% | 53.20% | 68.00% |
| Location | 47.02% | 46.36% | 68.21% |
Ablation Study: Impact of Key MoDora Components (AIC-Acc on MMDA)
Textual Evidence: Removing textual information leads to -12.77% accuracy drop, highlighting its critical role.
Locational Evidence: Excluding locational document regions results in a -5.07% accuracy drop, showing its importance for non-textual elements.
Forward Search: Disabling LLM-based forward search causes a -2.44% accuracy drop, confirming the value of title/metadata guided retrieval.
Tree Structure (CCTree): Without the hierarchical CCTree, accuracy drops significantly by -15.49%, demonstrating its effectiveness.
Component Construction: The most severe degradation (-37.09%) occurs without initial component reconstruction, proving its foundational importance.
Calculate Your Potential ROI
Estimate the potential annual cost savings and hours reclaimed by implementing MoDora's advanced semi-structured document analysis capabilities within your enterprise. This calculator helps visualize the efficiency gains across various industry contexts.
Annual Cost Savings
$0
Hours Reclaimed Annually
0
Your MoDora Implementation Roadmap
A clear path to integrating advanced semi-structured document analysis into your enterprise operations.
Phase 1: Discovery & Integration
Initial consultation, document type assessment, and seamless integration with existing OCR pipelines to begin component aggregation.
Phase 2: CCTree Customization & Optimization
Tailoring the Component-Correlation Tree (CCTree) to specific document layouts and semantic relationships, optimizing bottom-up summarization.
Phase 3: Retrieval Strategy & LLM Fine-tuning
Implementing and fine-tuning the question-type-aware retrieval mechanism, including LLM-guided pruning and cross-modal verification, for target use cases.
Phase 4: Pilot Deployment & Performance Monitoring
Deploying MoDora in a pilot environment, continuous monitoring, and iterative refinement based on real-world query performance and user feedback.
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