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Rohan Paul· @rohanpaul_ai · X· · Original publication time AI score47

OXR introduces an open-source 1.3B document parser, claiming top rankings on three benchmarksMachine translation

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AI introduction

During post-training, OXR uses a vision-language model to evaluate parsing outputs, identifies uncertainty through rollout disagreements, and generates data for the next training round based on clusters of failure causes. Its publisher claims the 1.3B model ranks first on three document-parsing benchmarks and delivers 3.05 times GLM-OCR's throughput on a single RTX PRO 6000.

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OXR makes parsing failures part of the training process.

During post-training (OXR is the first document parser to use RSI here), a VLM evaluates its outputs, rollout disagreements flag uncertainty, and a separate clustering model groups the causes;

those findings then guide targeted data generation for the next round, and the 1.3B model that comes out of it sits first on olmOCR-Bench, OmniDocBench and DocParsingBench while running 3.05× GLM-OCR's throughput on a single RTX PRO 6000, the fastest open-source result in the speed comparison.

Worth studying (Github link in comment)

QuoteSoMark@somarkai
Beyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench🏆 #1 in parsing speed among open-source models⚡ RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! 🌟 https://t.co/kQlsq8oIdN
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来源:Rohan Paul · x.com

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