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World rehearsal enables LLM agents to internalize environment dynamics, achieving superior performance without costly external interactions.
A/B Agent achieves a 4.829% boost in GMV by autonomously evolving recommendation strategies through a novel hierarchical knowledge organization and real-time feedback loop.
Agents can exhibit significant performance gains from retained experience, but the pathways to these improvements are often unclear and model-dependent.
Agent-as-a-Judge can outperform LLM-as-a-Judge in complex environments, but still struggles to reliably verify agent behavior, revealing a critical gap in current LLM-based agent evaluation.
