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该论文分析了仓库级编码基准中的过度规范与规范不足问题，指出将真值信息泄露到任务描述会形成"剧透"，并提出生成简洁提示的建议以提升基准的可靠性与持久性。
This paper investigates how adding demographic attributes in prompts affects LLM-human agreement across tasks, finding that while a few high-signal attributes improve alignment, over-specification degrades it. The study uses five open-source LLMs and neuron probing to show that attribute signal quality and coherence matter more than quantity.