MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding

Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models torecover long-form sequences containing layout, tables, and formulas. De-spite recent advances in vision-language models, most existing systemsrely on autoregressive decoding, which introduces sequential latency andamplifies error propagation in long documents. In this work, we revisitdocument OCR from an inverse rendering perspective, arguing that left-to-right causal generation is an artifact of serialization rather than anintrinsic property of the task. Motivated by this insight, we proposeMinerU-Diffusion, a unified diffusion-based framework that replaces au-toregressive sequential decoding with parallel diffusion denoising undervisual conditioning. MinerU-Diffusion employs a block-wise diffusion de-coder and an uncertainty-driven curriculum learning strategy to enablestable training and efficient long-sequence inference. Extensive experi-ments demonstrate that MinerU-Diffusion consistently improves robust-ness while achieving up to 3.2× faster decoding compared to autoregres-sive baselines. Evaluations on the proposed Semantic Shuffle benchmarkfurther confirm its reduced dependence on linguistic priors and strongervisual OCR capability.