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Abstract:Structured LLM workflows, where specialized LLM sub-agents execute in line with a predefined graph, have turn into a robust abstraction for fixing advanced tasks.
Optimizing such workflows, i.e., deciding on configurations for each sub-agent to balance accuracy and latency, is difficult due to the combinatorial design space over mannequin selections, reasoning budgets, and workflow buildings. Existing price-conscious methods largely deal with workflow optimization as a routing problem, selecting a configuration at inference time for each question in keeping with the accuracy-latency goal used throughout coaching.
We argue that structured LLM workflows may also be optimized from a compilation perspective: earlier than deployment, the system can globally explore the workflow design space and assemble a reusable set of workflow-stage configurations spanning various accuracy-latency commerce-offs.
Drawing inspiration from machine learning compilers, we introduce FlowCompile, a structured LLM workflow compiler that performs compile-time design area exploration to establish a high-quality, reusable trade-off set. FlowCompile decomposes a workflow into sub-brokers, profiles each sub-agent beneath numerous configurations, and composes these measurements by means of a construction-conscious proxy to estimate workflow-level accuracy and latency.

It then identifies numerous excessive-high quality configurations in a single compile-time pass, without retraining or online adaptation. Experiments across numerous workflows and difficult benchmarks present that FlowCompile consistently outperforms heuristically optimized workflow configurations and routing-primarily based baselines, delivering as much as 6.4x speedup.
The compiled configuration set additional serves as a reusable optimization artifact, enabling versatile deployment under varying runtime preferences and supporting downstream selection or routing.

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