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Beihang University, State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, ×10−51\times 10^{-5} for Stage-1 and Stage-2, respectively. In Stage-2, we fine-tune LoRA [25] with a rank of 1616 applied to the vision encoder. The batch size is fixed at 1616 for all experiments, and the total number of training steps is 46004600. Competitors. Our method enhances visual representation through diffusion-based reconstruction. We compare it with three state-of-the-art methods: DIVA [77], GenHancer [57], and un
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LLMs can now more accurately answer questions on complex documents thanks to a new system that understands layout and hierarchical relationships between document components.