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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.
arXiv:2608. 11741v1 Announce Type: cross Abstract: The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context.
arXiv:2608.23507v1 Announce Type: cross Abstract: Historical people may appear under different languages, scripts, and transcription traditions, while distinct individuals may share highly similar or...
Historical people may appear under different languages, scripts, and transcription traditions, while distinct individuals may share highly similar or even identical names. This makes historical identi...
CHisAgent is a multi‑agent large language model framework designed to build event taxonomies for ancient Chinese cultural contexts. It splits the task into three specialized stages: an Inducer that creates an initial hierarchy from raw historical texts, an Expander that adds missing intermediate concepts using LLM world knowledge, and an Enricher that incorporates external structured historical resources to ensure faithfulness. Applied to the Twenty‑Four Histories, CHisAgent produced a comprehensive, domain‑aware taxonomy covering politics, military, diplomacy, and social life, and evaluations show improved structural coherence, coverage, and cross‑cultural alignment.
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...