AI Research
SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs
Bridging the gap between generic AI agents and rigid, real-world enterprise knowledge graphs.
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing agentic approaches that perform well on public benchmarks often fail to generalize to real-world enterprise Knowledge Graphs (KGs)...
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ArXiv CS.AI
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ArXiv CS.AI
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SCAIR demonstrates that integrating schema constraints directly into reasoning loops significantly boosts the reliability of RAG systems in complex enterprise environments. This approach proves that domain-specific alignment often outperforms generic LLM capabilities when navigating structured, real-world data.
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