arXiv:2607.08269v1 Announce Type: new Abstract: Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We present PolyUQuest, a verifiable, structure-aware web RAG framework built on a heterogeneous graph that unifies hyperlink topology between pages, DOM hierarchy within pages, and entity-relation knowledge across pages. A two-tier router dispatches each query to one of three retrieval modes matched to its structural need, including direct block retrieval, cross-page graph traversal, and multi-hop entity
The increasing sophistication and integration of AI in web retrieval necessitate overcoming the limitations of treating web pages as flat text.
This development addresses a fundamental limitation in current RAG systems by leveraging the inherent structure of the web, potentially leading to significantly more accurate and verifiable AI-generated information.
AI systems can now better understand and utilize the rich structural and semantic signals within and across web pages, moving beyond simple keyword matching to contextual graph traversal.
- · AI developers
- · Search engines
- · Data scientists
- · Enterprise AI
- · Legacy RAG systems
- · Simple keyword search dependent applications
Improved accuracy and verifiability of AI-generated content through enhanced web data understanding.
Accelerated development of more complex and reliable AI agents capable of nuanced information retrieval and synthesis.
Potential for new business models built around verifiable, structure-aware information extraction from the web, impacting research and decision-making across industries.
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Read at arXiv cs.AI