HuggingFace Papers
ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering
agentictool-use
What happened
ProMSA is a progressive multimodal search agent designed for knowledge-based visual question answering. It dynamically selects search strategies and optimizes its retrieval paths using sequence-level reinforcement learning.
Why it matters
It demonstrates that dynamic, RL-optimized search strategies outperform static retrieval-augmented generation in complex multimodal tasks.
The take
The core contribution here is the adaptive selection of search strategies based on visual and textual context. For builders, this highlights the shift away from static RAG pipelines toward dynamic, agentic search strategies that decide *how* to search based on the query's complexity.
Do this
When building RAG systems, replace static search queries with an agentic router that can choose between keyword, semantic, or multi-step iterative search based on query complexity.
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