AI 中文总结
针对AI生成响应中广告评估与定价的核心挑战，该研究构建智能体模拟框架生成监督，提炼出高效评估器，实现更优的点击意图预测，还推导了最优支付规则并扩展了广告生成的训练目标。
AI 中文摘要
随着搜索日益转向大语言模型（LLM）驱动的问答引擎，广告正嵌入生成的响应本身，因此应从用户效用和商业价值两方面对其进行评估。核心挑战在于点击意图：行为日志不可用，人工标注难以校准，前沿LLM评判器会将意图与语言流畅度混淆。这些问题相互叠加，因为合理的定价预设了连续的意图信号，而生成该信号又预设了目前无法获得的监督。我们通过基于心理学的智能体模拟框架构建缺失的监督，并将其提炼为一个参数高效的评估器，该评估器可预测点击意图，同时生成广告质量的三个关联维度：平滑、可微的估计值。通过具有符号确定性的行为扰动验证，该评估器在相关性敏感性上超越了前沿零样本评判器（79%对比60%-67%），可追踪分级内容退化，对103个虚构产品的泛化无误差，且在五名标注者的成对判断中与人类偏好的一致性达86%，且一致性随评估器置信度提升而增加。基于其估计值，我们直接构建定价层，推导出使真实竞价最优的唯一支付规则，在best-of-k分配上进行演示，并将该机制扩展至非单调分配。该可微信号也可直接作为广告生成的训练目标。
英文摘要
As search increasingly shifts toward LLM-driven answer engines, advertising is becoming embedded within the generated response itself and should therefore be evaluated for both user utility and commercial value. The key challenge is click-through intent: behavioural logs are unavailable, human annotation resists calibration, and frontier LLM judges conflate intent with linguistic fluency. These gaps compound, as principled pricing presupposes a continuous intent signal, while generating such a signal presupposes supervision that is currently unavailable. We construct the missing supervision through a psychologically grounded agent simulation framework, and distil it into a parameter-efficient evaluator that predicts click-through intent, together with the three companion dimensions of ad quality, as smooth, differentiable estimates. Validated through sign-certain behavioural perturbations, the evaluator surpasses frontier zero-shot judges on relevance sensitivity (79% versus 60-67%), tracks graded content degradation, generalises without error to 103 fictional products, and agrees with human preference in 86% of pairwise judgements across five annotators, with agreement rising in the evaluator's confidence. Upon its estimates we build the pricing layer directly, deriving the unique payment rule under which truthful bidding is optimal, demonstrating it on a best-of-k allocation, and extending the mechanism to non-monotone allocations. The same differentiable signal stands ready as a training objective for ad generation.
Comments9 pages, 2 figures
