Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs explores A hybrid LLM-GNN system for advanced question answering over incomplete knowledge graphs.. Commercial viability score: 7/10 in AI-driven Knowledge Management.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Freshness
Canonical route: /paper/leveraging-llm-gnn-integration-for-open-world-question-answering-over-knowledge-graphs
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Agent Handoff
Canonical ID leveraging-llm-gnn-integration-for-open-world-question-answering-over-knowledge-graphs | Route /paper/leveraging-llm-gnn-integration-for-open-world-question-answering-over-knowledge-graphs
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/leveraging-llm-gnn-integration-for-open-world-question-answering-over-knowledge-graphsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.13979"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs",
"normalized_query": "2604.13979",
"route": "/paper/leveraging-llm-gnn-integration-for-open-world-question-answering-over-knowledge-graphs",
"paper_ref": "leveraging-llm-gnn-integration-for-open-world-question-answering-over-knowledge-graphs",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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