arXiv:2606.01613v2 Announce Type: replace-cross Abstract: This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning. Unlike conventional single-pass RAG systems, the proposed architecture uses an autonomous, evidence-gated pipeline that classifies query intent, generates separate text and visual query rewrites, performs hybrid text retrieval with FAISS and BM25 foll
The proliferation of complex, domain-specific literature and the advancements in multimodal AI are driving the need for more sophisticated reasoning frameworks to extract valuable insights efficiently.
Sophisticated AI agents capable of reasoning over vast multimodal technical literature empower faster innovation, reduce research cycles, and democratize access to specialized knowledge in critical fields.
This agentic RAG framework significantly enhances the ability of AI to understand and synthesize information from highly technical, domain-specific content, moving beyond simple keyword retrieval to evidence-gated reasoning.
- · AI researchers and developers
- · Technical R&D sectors (e.g., automotive, robotics)
- · Domain experts seeking specialized knowledge
- · Companies building advanced RAG solutions
- · Traditional, single-pass RAG systems
- · Manual literature review processes
- · Individuals/organizations reliant on general-purpose AI for specialized tasks
Improved efficiency and accuracy in technical literature review and knowledge synthesis within specialized domains.
Accelerated research and development cycles in fields where understanding complex technical documents is paramount, leading to quicker product innovation.
The development of highly specialized, fully autonomous AI research agents capable of discovering novel insights across disparate and complex technical knowledge bases.
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Read at arXiv cs.AI