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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:2604. 13731v2 Announce Type: replace Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.
arXiv:2608. 03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneous document elements distributed across multiple pages.
arXiv:2608. 08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.
arXiv:2606. 29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs).
Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning, but instead failing to locate critical details i...