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When Does Experience Memory Actually Help Closed-Loop LLM Driving Agents? A Controlled Evaluation

A controlled evaluation on the HighwayEnv testbed (1Hz, with gpt-4o-mini used for the key comparisons) shows that the published gains from experience memory in closed-loop LLM driving agents mainly come from sampling luck in the warmup pool: retrieval-based memory beat No-Memory in only one of ten pools (sign test p=0.011).

Part of the event: Controlled evaluation: experiential memory gains mostly come from warm-up pool sampling luck

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