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GNNs as Predictors of Agentic Workflow Performances

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GNNs as Predictors of Agentic Workflow Performances
The curator’s take

This work introduces FLORA-Bench, a large-scale benchmark to evaluate GNN-based predictors for automating and optimizing agentic workflows. It shows that Graph Neural Networks can efficiently predict the success of multi-agent LLM workflows, significantly reducing costly repeated model calls.

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