AI Research
BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data
Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls. We present B...
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ArXiv CS.AI
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ArXiv CS.AI
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BatchDAG addresses a critical LLM limitation by transforming complex analytical questions into executable DAGs, enabling scalable and cost-effective analysis of large enterprise datasets. This approach overcomes context windows and sequential processing bottlenecks, significantly improving efficiency and reducing costs for data-driven insights.
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