Multimodal review generation is essential in academic research. However, existing LLM-based models often struggle to pinpoint specific details within papers, yielding generalized, unsupported critiques that compromise final decisions. To address these limitations, we propose AnchorReviewer, a coarse-to-fine, evidence-grounded multimodal review generation framework. Central to our architecture is an integrated pipeline featuring an Inline Retrieval-Augmented Generation (InRAG) module. Combined with our Unified Embedding Module, InRAG facilitates the high-precision extraction of multimodal evidence chunks, ensuring our detailed reviews are strictly anchored to the original content. Furthermore, we incorporate a Reflection Mechanism to refine initial outputs and maximize decision accuracy. To support model evaluation and our Progressive Training Strategy, we construct the large-scale AnchorReview dataset, comprising diverse multimodal review samples. Experimental results demonstrate that AnchorReviewer significantly improves review reliability, excelling in critical evaluation, multimodal understanding, and decision consistency. Our code and dataset will be publicly available.

Recent advancements in digital humanities have intensified the demand for intelligent processing of ancient Chinese texts, particularly across specialized domains such as historical records and ancient medical literature. Among related research areas, Named Entity Recognition (NER) plays a crucial role, serving as the foundation for knowledge graph construction and deeper humanities computing studies. In this paper, we introduce a architecture specifically designed for multi-domain ancient Chinese NER tasks based on a pre-trained language model (PLM). Building upon the GujiRoberta backbone, we propose the GujiRoberta-BiLSTM-Attention-CRF model. Experimental results on three distinct domain-specific datasets demonstrate that our approach significantly outperforms the official baselines across all three datasets, highlighting the particular effectiveness of integrating an attention mechanism within our architecture.