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arXiv · 2606.27974

ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering

ZhengXian Wu·Hangrui Xu·Kai Shi·Zhuohong Chen·Yunyao Yu·Chuanrui Zhang·Zirui Liao·Jun Yang·Zhenyu Yang·Haonan Lu·Haoqian Wang
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Abstract

Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge. Most prior methods use a fixed retrieve-then-generate pipeline with a pre-selected retriever and a static top-k setting, which is not adaptive during reasoning. We propose ProMSA, a progressive multimodal search agent for KB-VQA. Given an image-question pair, the agent iteratively chooses image search, text search, or stop, under explicit tool-call budgets and with deduplication to avoid redundant retrieval. For training, we first use rejection-sampling SFT to learn valid tool-use formats, then optimize the agent with TN-GSPO, a sequence-level RL objective that normalizes updates by both generation length and tool-interaction depth. Experiments on E-VQA and InfoSeek show consistent gains over strong RAG and agent baselines, and improved retrieval and end-to-end accuracy. The code is available at https://github.com/DingWu1021/Promsa.

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ZhengXian Wu, Hangrui Xu, Kai Shi, Zhuohong Chen, Yunyao Yu, Chuanrui Zhang, Zirui Liao, Jun Yang, Zhenyu Yang, Haonan Lu, Haoqian Wang. 2026-06-26. ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering. https://arxiv.org/abs/2606.27974

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