SAGE: From Direct Answering to Evidence-Grounded Inference for Chinese Ancient Document Understanding

• URL: http://arxiv.org/abs/2608.24011v1
• Date: Tue, 25 Aug 2026 03:01:51 GMT
• Title: SAGE: From Direct Answering to Evidence-Grounded Inference for Chinese Ancient Document Understanding
• Abstract summary: We propose SAGE, an evidence-grounded multi-agent framework for Chinese ancient document understanding.<n>SAGE coordinates specialized agents for task-aware planning, tool-mediated evidence acquisition, claim-level verification, and bounded replanning.<n> Experiments on the AncientDoc benchmark show that SAGE consistently outperforms matched direct-answering baselines.
• Score: 61.02068339726333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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