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Agentic DAG-Orchestrated Planner Framework for Multi-Modal, Multi-Hop Question Answering in Hybrid Data Lakes
arXiv – CS AI|Kirushikesh D B, Manish Kesarwani, Nishtha Madaan, Sameep Mehta, Aldrin Dennis, Siddarth Ajay, Rakesh B R, Renu Rajagopal, Sudheesh Kairali|
🤖AI Summary
Researchers introduce A.DOT Planner, an AI framework that enables multi-hop question answering across hybrid data lakes containing both structured and unstructured data. The system uses directed acyclic graphs to orchestrate complex queries, achieving 14.8% better accuracy and 10.7% better completeness than existing solutions.
Key Takeaways
- →A.DOT Planner addresses the critical gap in multi-hop reasoning for enterprise question answering systems.
- →The framework compiles natural language queries into DAG execution plans that span structured and unstructured data sources.
- →Advanced caching with paraphrase-aware template matching enables reuse of execution plans for faster query processing.
- →The system provides explicit evidence trails for verification and data lineage tracing to build user trust.
- →Benchmark results show significant improvements in both correctness and completeness over current RAG-based solutions.
#ai#question-answering#data-lakes#multi-modal#enterprise-ai#natural-language-processing#dag#query-optimization#research#arxiv
Read Original →via arXiv – CS AI
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