We present AnandaSky, a vision–language model for line-level transcription of historical sinographic documents. The model combines a compact high-resolution visual encoder with global attention, 10px patches, uncompressed visual prefix and a Qwen3-0.6B autoregressive decoder. It is trained at scale on 4M annotated lines from documents produced in China and Korea between the 8th and 20th centuries. Across in-domain and held-out public benchmarks, AnandaSky achieves sub-1% CER on five of eight datasets, sets a new state of the art on MTHv2 with 0.92% CER, and shows strong transfer to unseen collections. For EvaHan 2026, full fine-tuning on the organizers’ data to match task-specific annotation conventions reduces CER relative to the official baseline by 5.2% on prints and 12.1% on manuscripts, despite using one-tenth as many parameters.

We present the Named Entity Recognition sys-tem developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach in-tegrates three core components: (1) Pindola, a modern transformer-based bidirectional en-coder pretrained on a large corpus of Classi-cal Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points.