Ancient Chinese text digitization faces challenges like variant characters and complex layouts. Based on the EvaHan 2026 tasks, this study proposes an LVLM-based framework for printed/handwritten text recognition and layout analysis. To effectively adapt the Qwen2.5-VL-7B-Instruct model, our methodology innovates through a dual-level optimization strategy: distinct augmentation strategies are developed for OCR and layout tasks, while task-specific prompt templates are engineered to decouple text transcription from coordinate prediction. This combined approach significantly enhances overall task proficiency, achieving Character Error Rates of 0.0372 (printed) and 0.0823 (handwritten), alongside a mean average Precision of 0.2933 for layout analysis. Results show general LVLMs underperform in zero-shot ancient text tasks, but fine-tuning with tailored strategies significantly boosts performance and highlights their potential.

Matching patients to clinical trials is a critical bottleneck hindered by complex eligibility criteria. While conversational AI offers a promising solution, its safe deployment depends on high-quality, domain specific data. This paper introduces three benchmark datasets designed to support the development and evaluation of conversational agents for clinical trial pre-screening. First, a manually-annotated paired-criterion dataset provides a gold standard for structuring raw criteria, which we used to objectively group 12,596 criteria. Second, we curated a human-authored question benchmark to validate the clinical fidelity and patient-centric clarity of questions generated by a medical LLM, ensuring the AI’s dialogue is accurate and understandable. Third, we constructed a human-validated assessment corpus of criterion-question-answer tuples with human-labeled outcomes to evaluate criterion classification based on a patient’s answer to a generated question. The primary contribution of this work is a foundational set of benchmark datasets, designed to support and evaluate key components for a chatbot for clinical trial search.

Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment. This study aims to evaluate the generalizability of eligibility classification across a broad spectrum of clinical trials. Starting with phase 3 cancer trials, annotated with seven eligibility exclusions, then to determine how well models can generalize to non-cancer and non-phase 3 trials. To assess this, we have compiled eligibility criteria data for five types of trials: (1) additional phase 3 cancer trials, (2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes trials, and (5) observational trials for any disease, comprising 2,490 annotated eligibility criteria across seven exclusion types. Our results show that models trained on the extensive cancer dataset can effectively handle criteria commonly found in non-cancer trials, such as autoimmune diseases. However, they struggle with criteria disproportionately prevalent in cancer trials, like prior malignancy. We also experiment with few-shot learning, demonstrating that a limited number of disease-specific examples can partially overcome this performance gap. We are releasing this new dataset of annotated eligibility statements to promote the development of cross-disease generalization in clinical trial classification.