arXiv:2608. 11741v1 Announce Type: cross Abstract: The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context.
The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis.
Ancient-Bench is a new benchmark for recognizing text on ancient Chinese artifacts, comprising 2,700 images that span 3,000 years of character evolution, nine artifact categories, and seven historical script forms. It introduces three annotation standards—symbol, character, and parsing standardization—to accommodate medium‑specific characteristics and enable consistent evaluation. Experiments show that current Vision‑Language Models and OCR specialists still struggle with variant characters, specialized symbols, and hallucination, indicating the task remains largely unsolved.
arXiv:2609.37755v1 Announce Type: new Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would...
arXiv:2608. 07917v1 Announce Type: new Abstract: Chinese historical documents preserve valuable cultural heritage, but many collections remain accessible only as scanned page images, preventing full-text retrieval, collation, and computational analysis.
arXiv:2608. 07917v2 Announce Type: replace Abstract: Chinese historical documents preserve valuable cultural heritage, but many collections remain accessible only as scanned page images, preventing full-text retrieval, collation, and computational analysis.
arXiv:2608.29133v1 Announce Type: new Abstract: History is not preserved in complete, continuous form. Accounts of a person's activities, relationships and historical contexts are scattered across te...
Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing hallucination benchmarks often provide response-level labels, with limited support for identifying the exact erroneous content, explaining why it is incorrect, or selecting the correct factual answer.
SAGE is a multi‑agent framework that transforms Chinese ancient document understanding from direct answer generation into evidence‑grounded inference. It orchestrates specialized agents for planning, evidence acquisition, claim verification, and bounded replanning within a shared‑state runtime, enabling evidence seeking, answer revision, and abstention when grounding is lacking. Experiments on the AncientDoc benchmark show that SAGE outperforms direct‑answering baselines across three LVLM backbones, and even a 9B‑parameter Qwen3.5 model surpasses larger monolithic LVLMs, underscoring the value of structured, evidence‑grounded inference over mere model scaling.
arXiv:2606. 12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
Chinese ancient document understanding demands complex visual, linguistic, and historical reasoning. Current Large Vision-Language Models (LVLMs) typically rely on an opaque, single-pass generation pa...
arXiv:2608.28635v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have advanced document understanding, visual question answering, and text extraction. However, their...
CNeo-Bench is a new benchmark comprising 4,759 Chinese neologisms, each with reference definitions and categorized by linguistic mechanisms such as phonetic substitution and visual character decomposition. The benchmark includes a two-tier evaluation framework that tests whether models can describe a neologism and whether they can manipulate its underlying mechanism. Evaluation of 18 large language models shows that most perform poorly on definition generation (below 40%) and exhibit a recognition‑manipulation gap, often paraphrasing rather than restoring the original form; few‑shot prompting helps but does not fully resolve the errors.