arXiv:2607.07836v1 Announce Type: new Abstract: We present Infinity-Parser2, a large multimodal model that couples a controllable data-synthesis pipeline with multi-task reinforcement learning for end-to-end document parsing, addressing the persistent scarcity of faithfully annotated parsing corpora. Our contributions are threefold. First, we build a scalable synthesis engine, pairing a controllable rendering framework with an iterative refinement loop, and use it to construct and open-source Infinity-Doc2-5M: a 5-million-sample bilingual (Chinese/English) corpus spanning diverse document type
The development of advanced multimodal models capable of synthesizing high-quality, specialized data is emerging as a critical bottleneck to broader AI adoption and capability expansion.
This breakthrough addresses the persistent challenge of data scarcity for document parsing, accelerating the development of reliable AI systems for enterprise automation and information extraction.
The availability of large-scale synthetic corpora like Infinity-Doc2-5M significantly lowers the barrier to entry for developing and deploying sophisticated document AI solutions, reducing reliance on costly manual annotation.
- · AI developers
- · Enterprise automation
- · Multimodal AI research
- · Data synthesis platforms
- · Manual data annotation services
- · Companies with proprietary data MOATs
- · Legacy OCR providers
Improved performance and broader application of document parsing AI across various industries.
Increased efficiency in back-office operations and accelerated digital transformation for businesses handling large volumes of documents.
Potential for new business models built around fully automated document processing and information extraction services, impacting white-collar work.
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Read at arXiv cs.AI