全部动态

Docling v2.149.0 just shipped and who should update depends on what you feed it 📄 Building RAG over PDFs? New opt-in --use-embedded-picture-images pulls a figure straight out of the PDF at native pixels instead of a rendered crop. If the region also holds text or vector shapes it falls back to the crop Need OCR on one consumer GPU? New minicpm_ocr preset for OpenBMB's 1.3B MiniCPM-OCR (English + Chinese): docling doc.pdf --pipeline vlm --vlm-model minicpm_ocr Transcribing audio on a Mac? ASR can now run on mlx-audio with no torch, and Granite Speech recordings get split into ~30s windows ⚠️ Scripts: --vlm-model X now really switches to the VLM pipeline (it used to be silently ignored), and pairing it with --pipeline standard now errors pip install -U docling https://github.com/docling-project/docling/releases/tag/v2.149.0

LiteParse integrates with MiniCPM-OCR from @openBMB 🚀 MiniCPM-OCR is a new lightweight OCR model that performs OCR after capturing cropped layout regions. On ParseBench, it beats Gemini 3.5 Flash Lite and PaddleOCR-VL-1.6 on content faithfulness. LiteParse is our ultra-fast OSS document parser. When OCR is required, LiteParse lets you connect any existing OCR model. See the integration: https://github.com/run-llama/liteparse/tree/main/ocr/minicpmocr

Every Agent API effort level now costs less with the same or higher research quality. Effort levels are bundled agent configurations, optimized for specific tasks. A new ultra level is available for the most demanding, long-running requests. https://pplx.ai/effort-levels

Tencent Hy4 from @TencentHunyuan is now supported in OpenClaw. Vision, a 1M-token context window, and improved coding and long-horizon agent capabilities. Available through Tencent TokenHub, TokenPlan, and @OpenRouter. https://hy.tencent.ai/research/hy4

OCR pipelines can be much more reliable when you add a verification model. Instead of trusting every extracted result, you can use a second model to check how accurate the output actually is. A good example is MiniCPM OCR from @OpenBMB and ModelBest. It combines a lightweight document parser with a separate model, MiniCPM OCR Jev, designed to evaluate OCR reliability. Here’s how it works: 1. Document parsing MiniCPM-OCR uses just 1.3B parameters to extract information from Chinese and English documents. It handles scanned documents, phone photos, curved pages, tilted inputs, and even key information extraction. 2. Output verification MiniCPM-OCR Jev evaluates the extracted text, formulas, and tables to estimate how reliable the results are. You can also use it to verify outputs from other OCR models, meaning you don’t have to replace your existing pipeline. Why does this matter for AI agents? Agents are increasingly working with PDFs, invoices, research papers, and other structured documents. But one incorrect number or poorly extracted table can affect every step that follows. A simple workflow would look like this: → Parse the document with MiniCPM-OCR. → Pass the extracted content to Jev for reliability scoring. → Route low-confidence results to a human reviewer or another correction step. One important distinction: Jev identifies potentially unreliable outputs. It doesn’t automatically correct them. The project also provides open weights, fine tuning support, and inference compatibility with llama.cpp, vLLM, SGLang, and Ollama. There are also integrations through Agent Skills. If you’re building document processing agents, this is worth testing. Especially on difficult inputs like dense tables, mathematical formulas, scanned pages, and low quality phone photos. The bigger idea: Don’t just build agents that extract information. Build pipelines that can also evaluate whether that information should be trusted.