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This paper introduces LIMEN, an LLM-guided evolutionary framework that automatically discovers reinforcement learning interfaces by jointly optimizing observation mappings and reward functions from raw simulator states. The approach reduces manual engineering effort and demonstrates that co-designing observations and rewards outperforms optimizing either component alone.