让AI读懂古文不再靠猜，而是像侦探一样找证据、验结论。
SAGE: From Direct Answering to Evidence-Grounded Inference for Chinese Ancient Document Understanding
- 用多个专业AI agent协作，分步推理而非直接回答。
- 在AncientDoc数据集上超越更大模型，关键指标提升12.3%。
- 适合需要高可信度解读的古籍研究与数字人文项目。
中文古籍理解需融合视觉、语言与历史推理。现有大视觉语言模型多采用单次生成的黑箱模式，常产生过度自信且缺乏依据的回答。为此，我们提出SAGE——一种基于证据的多智能体框架，将古籍理解重构为证据支撑的推理过程。SAGE通过任务规划、工具辅助获取证据、逐项验证主张以及受限状态下的动态重规划，实现有限证据搜索、答案修正与必要时拒答。在AncientDoc基准测试中，SAGE在三种LVLM骨干网络下均优于直接回答基线。尤为突出的是，使用Qwen3.5-9B的SAGE在多数指标上超越更大规模的单体模型，凸显结构化证据推理对提升性能的重要性，远超单纯模型扩展。
原文摘要 · Abstract (English)
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 paradigm, often producing overconfident and weakly grounded responses. To address this, we propose SAGE, an evidence-grounded multi-agent framework that reformulates Chinese ancient document understanding as evidence-grounded inference rather than direct answer generation. SAGE coordinates specialized agents for task-aware planning, tool-mediated evidence acquisition, claim-level verification, and bounded replanning under a constrained shared-state runtime. This design supports bounded evidence seeking, answer revision, and abstention when grounding is insufficient. Experiments on the AncientDoc benchmark show that SAGE consistently outperforms matched direct-answering baselines across three LVLM backbones. Remarkably, SAGE with Qwen3.5-9B surpasses much larger monolithic LVLMs on most evaluated metrics, highlighting the importance of structured, evidence-grounded inference beyond model scaling.
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