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LlamaIndex 发布 Extract v2.5，一个面向结构化文档提取的智能体架构，在不涨价的前提下将 ExtractBench 上各层级精度与 grounding 分数大幅提升，并显著优于前沿模型的价格/性能比。
LlamaIndex 发布 Extract v2.5，推出新一代基于 schema 的文档抽取 agent，可跨多页表格进行结构化推理并提供高级引用定位，在 ExtractBench 各档位上准确率与 grounding 分数均大幅提升，且每页定价不变。
LlamaIndex introduces Extract v2.5, a set of frontier-agent document extraction models (Cost Effective, Agentic, Agentic Plus) that now claim SOTA price-performance on ExtractBench, outperforming Opus 5.5 and GPT-6 Sol at 30%-4x lower cost, with major accuracy gains on long lists, multi-page records and scanned forms, plus new Advanced Citations and Structural Reasoning features available on LlamaParse.
A practical guide on using confidence scores in document extraction to balance automation and accuracy by setting thresholds for precision and recall.
LlamaIndex has launched LlamaExtract Agentic Plus, an advanced AI engine for extracting data from complex documents like long tables and forms, and is promoting their LlamaParse service for improved document processing.
LlamaParse introduces calibrated confidence scores for schema-guided document extraction, supporting various data types and providing bounding boxes to enhance human review and automation workflows.
Mizan introduces a national benchmark for evaluating large language models on Iraqi Arabic and civic context, highlighting gaps in MSA-focused evaluations and revealing issues like over-refusal in safety-hardened models.
LlamaIndex has launched Extract Turbo, a feature that enables lightning-fast extraction of structured data from documents with high speed and accuracy.
Jerry Liu demonstrates LlamaIndex's Astra agent autonomously generating a full product demo video via computer use and browser automation, while showcasing LlamaParse's new Agentic Plus mode for extracting dozens of fields from complex financial documents.
Demonstration of the KernelAI mobile app's capability to perform local document extraction on 52 pages using Arctic Embed and Bonsai 8B models on an iPhone 16, with new features in v2 including web search and custom model import.
The article highlights a common issue in document extraction pipelines where large documents cause silent record loss, and recommends using completeness checks and tools like llamaparse to maintain data integrity.
The article summarizes findings from the ParseBench paper, indicating that coding agents like Claude Code and Codex offer cost-effective baselines for schema-guided document extraction, especially on longer documents, compared to specialized OCR tools.
The paper diagnoses three failure modes in per-field selective risk control for document extraction systems and introduces a validity ladder of fixes, demonstrating improvements through experiments on real-world data with frontier AI models.
ExtractBench is a new benchmark for document extraction that strictly scores grounding accuracy, showing VLMs and coding agents lack evidence while LlamaExtract Agentic Plus leads with high page-level and word-level performance.
This paper investigates whether document extraction difficulty can be predicted before inference using low-cost features, allowing a router to select between a cheap and a strong model. Routing cut costs by up to 77% in some genres while maintaining quality, but only when difficulty varies and is predictable from document features.
Xberg is a local content intelligence framework (Rust core, MIT) that extracts text from 101 document formats via MCP server or CLI, reconstructs reading order and tables, and chunks for context windows—all on-device for AI agents.
ExtractBench is a new benchmark for schema-guided enterprise document extraction, evaluating value accuracy, record completeness, grounding, and cost across 4,869 pages of enterprise documents. The authors find that commercial VLMs struggle with long documents while coding agents are more accurate but costly, and LlamaExtract AgenticPlus leads on all metrics.
Introduces DECODEM, benchmark datasets for evaluating automated extraction of corporate governance variables from legal documents using large language models, showing high accuracy for many provisions.
Kreuzberg, a local document extraction tool, is being renamed to Xberg and its current version is on Long-Term Support (LTS).
Google released LangExtract, an open-source and free document extraction tool that outperforms expensive enterprise alternatives.