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

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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.24011 [cs.CL]

(or arXiv:2608.24011v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.24011 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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