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LLM agents struggle to translate general capabilities into cost-efficient, reusable solutions, often failing to outperform simple distillation baselines despite significant zero-shot prowess.
Framework choice in multi-agent systems matters just as much as the LLM itself, a fact obscured by existing model-centric benchmarks.