Long-document OCR is bottlenecked by page chunking and growing KV cache. A widely shared post says Baidu’s Unlimited-OCR uses 3B total parameters, 500M active parameters, and a 32K context window to read 40-page documents in one pass.
The r/MachineLearning post drew attention because OCR is becoming a measurable ingestion layer for agents and RAG, not just a text extraction demo.
Document AI is shifting from plain text extraction to structured evidence for retrieval and verification. Mistral says OCR 4 supports 170 languages and reaches 85.20 on OlmOCRBench.
r/MachineLearning paid attention because the benchmark did not just crown a winner. It argued that many teams are overpaying for document extraction, then backed that claim with repeated runs, cost-per-success numbers, and a leaderboard where several cheaper models outperformed pricey defaults.
A LocalLLaMA post points to IBM's Granite-4.0-3B-Vision, a compact VLM built for charts, tables, and document key-value extraction rather than generic multimodal chat.
A post on r/LocalLLaMA highlighted Kreuzberg v4.5, a Rust-based document intelligence framework that now adds stronger layout and table understanding. The release claims Docling-level quality with lower memory overhead and materially faster processing.