Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientific publishing.We study page-level reconstruction of scientific PDFs into compilable LaTeX and introduce TEX-OCR-Bench, a benchmark, and TEXOCR-Train, a large-scale training corpus, for this task.TEXOCR-Bench features a multi-dimensional evaluation suite that jointly assesses transcription fidelity, structural faithfulness, and endto-end compilability.Leveraging TEXOCR-Train, we train a 2B-parameter model, TEX-OCR, using supervised fine-tuning (SFT) and reinforcement learning (RL) with verifiable rewards derived from LaTeX unit tests that directly enforce compilability and referential integrity.Experiments across 21 frontier models on TEXOCR-Bench show that existing systems frequently violate key document invariants, including consistent section structure, correct float placement, and valid label-reference links, which undermines compilation reliability and downstream usability.Our analysis further reveals that RL with verifiable rewards yields consistent improvements over SFT alone, particularly on structural and compilation metrics.

We introduce SCIVER, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context.SCIVER consists of 3,000 expert-annotated examples over 1,113 scientific papers, covering four subsets, each representing a common reasoning type in multimodal scientific claim verification.To enable fine-grained evaluation, each example includes expert-annotated supporting evidence.We assess the performance of 21 state-of-the-art multimodal foundation models, including o4mini, Gemini-2.5-Flash,Llama-3.2-Vision, and Qwen2.5-VL.Our experiment reveals a substantial performance gap between these models and human experts on SCIVER.Through an in-depth analysis of retrieval-augmented generation (RAG), and human-conducted error evaluations, we identify critical limitations in current open-source models, offering key insights to advance models' comprehension and reasoning in multimodal scientific literature tasks.