#document-analysis News & Analysis

8 articles tagged with #document-analysis. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

8 articles

AIBearisharXiv – CS AI · May 277/10

🧠

A Universal Cliff and a Design Fingerprint: Cross-Section Defect Detection Under LLM Orchestration

Researchers discovered that large language models fail catastrophically at detecting contradictions spanning multiple sections of documents when using multi-agent orchestration systems, despite performing well in single-agent scenarios. The detection failure is universal across model families and generations, and alignment improvements don't fix the structural problem—creating a critical vulnerability in production LLM systems.

AIBullisharXiv – CS AI · May 97/10

🧠

Agentic Retrieval-Augmented Generation for Financial Document Question Answering

Researchers introduce FinAgent-RAG, an advanced AI framework designed to answer complex financial questions by combining iterative retrieval, reasoning, and self-verification. The system achieves 76-78% accuracy on financial benchmarks while reducing computational costs by 41%, demonstrating practical viability for institutional financial analysis.

AIBullisharXiv – CS AI · Apr 157/10

🧠

CropVLM: Learning to Zoom for Fine-Grained Vision-Language Perception

Researchers introduce CropVLM, a reinforcement learning-based method that enables Vision-Language Models to dynamically focus on relevant image regions for improved fine-grained understanding tasks. The approach works with existing VLMs without modification and demonstrates significant performance gains on text recognition and document analysis without requiring human-labeled training data.

AIBullisharXiv – CS AI · Mar 46/102

🧠

ScaleDoc: Scaling LLM-based Predicates over Large Document Collections

ScaleDoc is a new system that enables efficient semantic analysis of large document collections using LLMs by combining offline document representation with lightweight online filtering. The system achieves 2x speedup and reduces expensive LLM calls by up to 85% through contrastive learning and adaptive cascade mechanisms.

AINeutralarXiv – CS AI · Jun 56/10

🧠

MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA

Researchers introduce MARDoc, a Memory-Aware Refinement Agent framework that improves multimodal long-document question answering by decoupling the task into three specialized agents (Explorer, Refiner, Reflector) that maintain structured memory instead of accumulated interaction history. The approach reduces context noise while preserving critical evidence, outperforming baseline systems on benchmark datasets.

AINeutralarXiv – CS AI · Apr 106/10

🧠

Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs

Researchers introduce improved methods for detecting inconsistencies in documents using large language models, including new evaluation metrics and a redact-and-retry framework. The work addresses a research gap in LLM-based document analysis and includes a new semi-synthetic dataset for benchmarking evidence extraction capabilities.

AIBullisharXiv – CS AI · Mar 126/10

🧠

A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classification

Researchers developed a two-stage AI architecture using LLaMA-3.1-8B-Instruct and Legal-Roberta-Large models to automate the analysis of Non-Disclosure Agreements (NDAs). The system achieved high accuracy with ROUGE F1 of 0.95 for document segmentation and weighted F1 of 0.85 for clause classification, demonstrating potential for automating legal document analysis.

AIBullisharXiv – CS AI · Feb 276/105

🧠

MoDora: Tree-Based Semi-Structured Document Analysis System

Researchers introduce MoDora, an AI-powered system that uses tree-based analysis to understand and answer questions about semi-structured documents containing mixed data elements like tables, charts, and text. The system addresses challenges in processing fragmented OCR data and hierarchical document structures, achieving 5.97%-61.07% accuracy improvements over existing baselines.