arXiv:2606.27974v1 Announce Type: cross 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, w
The rapid advancement in multimodal AI and the increasing demand for more sophisticated, context-aware AI systems are driving the development of agentic approaches.
This development pushes AI closer to human-like reasoning by enabling adaptive information retrieval and integration, which is crucial for complex tasks like knowledge-based visual Q&A and broader AI applications.
AI systems can now dynamically search across image and text modalities, rather than relying on fixed retrieval pipelines, leading to more robust and accurate responses.
- · AI researchers and developers
- · Companies building knowledge-based AI systems
- · Users of complex AI applications
- · Generative AI platforms
Improved performance and broader applicability of AI systems in tasks requiring complex reasoning over diverse data.
Accelerated development of more generalized and autonomous AI agents capable of self-correcting and adapting their information gathering strategies.
Potential for AI to perform higher-level cognitive tasks currently limited to human experts, particularly in fields dependent on large, disparate knowledge bases.
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Read at arXiv cs.AI