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This work presents AgentServeSim, a simulator whose unit of execution is the agent program, and proposes LLM-driven automated agent-serving policy search using AgentServeSim as a CPU-based fitness evaluator.
Gated-Memory Routing is proposed, which conditions each decision on the query and a learned execution memory, and attains the best average accuracy, exceeding the strongest baseline by 2.44 points, while reducing HumanEval inference cost by 31.9% relative to that baseline.
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